This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee.
Project Acronym: DataBio
Grant Agreement number: 732064 (H2020-ICT-2016-1 – Innovation Action)
Project Full Title: Data-Driven Bioeconomy
Project Coordinator: INTRASOFT International
DELIVERABLE
D1.1 – Agriculture Pilot Definition
Dissemination level PU -Public
Type of Document Report
Contractual date of delivery M06 – 30/6/2017
Deliverable Leader LESPRO
Status - version, date Final – v1.1, 26/4/2018
WP / Task responsible WP1
Keywords: Agriculture, pilot, big data, modelling, user analysis,
user requirements, stakeholders
D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.1, 26/4/2018
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Executive Summary The objective of WP1 Agriculture pilot is to demonstrate how the Big data technologies will
be integrated into the pilots, in order to validate the Big data technologies on practical cases
from agriculture and how it can fulfil the end user communities’ expectations. The Big
technologies will be tested in three areas: arable farming, horticulture and Subsidies an
insurance, where every area will be tested in in pilots with different topics and running in
different countries.
Task 1.1 Co-innovative preparations deal with user understanding specifying the needs of
users and different stakeholders and its main objective is to analyse a set of functional and
non-functional requirements specified from the analysis of the pilot cases. Opportunities for
different solution technologies were reviewed with stakeholders and users are used as an
input and a set of scenarios are described within the bio-economy domain related to the
agriculture sector. Functional requirements are defined and used as input for the application
specification, development and piloting. User and stakeholder study to specify the (most
beneficial) areas of interest from different point-of-views and resulting to detailed scenario
building of the application scenarios from which use cases are defined. This subtask feeds
from user and stakeholder study as input.
The results are the pilot cases definitions including requirements specifications and
evaluation plans.
The organizations that were planned to participate in this task, and their respective planned
work effort in person-months, are Lespro (2), Intrasoft (5), VTT (3), IBM (2), Softeam (2),
Limetri (2), CREA (2), Fraunhofer (6), Vito (6), Tragsa (6), NP (2), Federu (6), CSEM (2), Rikola
(4), Novam (6), EXUS (6), CERTH (6), CITOLIVA (3), GAIA (9), ZETOR (8), CAC (6)
The deliverable D1.1 Agriculture Pilot Definition specifies the pilot case definitions,
requirement specifications, as well as implementation and evaluation plans.
D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.1, 26/4/2018
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Deliverable Leader: Karel Charvát (LESPRO)
Contributors:
Karel Charvát jr (LESPRO), Šárka Horáková (LESPRO), Savvas
Rogotis (NP), Antonella Cattuci (e-GEOS), Per Gunnar Auran
(SINTEF Fishery), Athanasios Poulakidas (INTRASOFT), Ephrem
Habyarimana (CREA), Pilot leaders
Reviewers: Fabiana Fournier (IBM), Tomas Mildorf (UWB), Caj Södergård
(VTT)
Approved by: Athanasios Poulakidas (INTRASOFT)
Document History
Version Date Contributor(s) Description
0.1 20/04/2017 Initial draft
0.2 18/06/2017 Content transferred to new template
0.3 20/06/2017 Pilot descriptions inserted, ArchiMate
diagrams added
0.4 29/06/2017 Final completing all chapters and
formatting
1.0 30/06/2017 Compliance to submission format and
minor changes.
1.1 26/04/2018 Savvas Sogotis,
Athanasios
Poulakidas
Updated Chapter 8 to reflect the new pilot
area. Consequently, changed “Cereals and
biomass crops” to “Cereal, biomass and
cotton crops” in the titles of B1, B1.1,
B1.2, B1.3, B1.4. This version is
resubmitted.
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Table of Contents EXECUTIVE SUMMARY ..................................................................................................................................... 2
TABLE OF CONTENTS ........................................................................................................................................ 4
TABLE OF FIGURES ........................................................................................................................................... 8
LIST OF TABLES ................................................................................................................................................ 9
DEFINITIONS, ACRONYMS AND ABBREVIATIONS ........................................................................................... 11
INTRODUCTION .................................................................................................................................... 13
1.1 PROJECT SUMMARY ..................................................................................................................................... 13 1.2 DOCUMENT SCOPE ...................................................................................................................................... 16 1.3 DOCUMENT STRUCTURE ............................................................................................................................... 16
SUMMARY ............................................................................................................................................ 17
2.1 OVERVIEW ................................................................................................................................................. 17 2.2 PILOT INTRODUCTIONS ................................................................................................................................. 17 2.3 OVERVIEW OF PILOT CASES ............................................................................................................................ 18 2.4 AGRICULTURE DATASETS UTILIZED IN PILOTS ...................................................................................................... 22 2.5 REPRESENTATION OF PILOT CASES ................................................................................................................... 22 2.6 PILOT MODELLING FRAMEWORK ..................................................................................................................... 22
PILOT 1 [A1.1] PRECISION AGRICULTURE IN OLIVES, FRUITS, GRAPES ................................................... 27
3.1 PILOT OVERVIEW ......................................................................................................................................... 27 3.1.1 Pilot introduction .......................................................................................................................... 27 3.1.2 Pilot overview................................................................................................................................ 27
3.2 PILOT CASE DEFINITION ................................................................................................................................. 29 3.2.1 Stakeholder and user stories ......................................................................................................... 32 3.2.2 Motivation and strategy ............................................................................................................... 32
3.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 33 3.3.1 Agriculture pilot A1.1 Motivation view ......................................................................................... 33 3.3.2 Agriculture pilot A1.1 Strategy view ............................................................................................. 33
3.4 PILOT EVALUATION PLAN .............................................................................................................................. 34 3.4.1 High level goals and KPI's ............................................................................................................. 34 3.4.2 Initial roadmap ............................................................................................................................. 35
3.5 BIG DATA ASSETS ......................................................................................................................................... 35
PILOT 2 [A1.2] PRECISION AGRICULTURE IN VEGETABLE SEED CROPS ................................................... 37
4.1 PILOT OVERVIEW ......................................................................................................................................... 37 4.1.1 Pilot introduction .......................................................................................................................... 37 4.1.2 Pilot overview................................................................................................................................ 37
4.2 PILOT CASE DEFINITION ................................................................................................................................. 38 4.2.1 Stakeholder and user stories ......................................................................................................... 41 4.2.2 Motivation and strategy ............................................................................................................... 41
4.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 41 4.3.1 Agriculture pilot A1.2 Motivation view ......................................................................................... 41 4.3.2 Agriculture pilot A1.2 Strategy view ............................................................................................. 42
4.4 PILOT EVALUATION PLAN .............................................................................................................................. 43 4.4.1 High level goals and KPI's ............................................................................................................. 43 4.4.2 Initial roadmap ............................................................................................................................. 43
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4.5 BIG DATA ASSETS ......................................................................................................................................... 43
PILOT 3 [A1.3] PRECISION AGRICULTURE IN VEGETABLES_2 (POTATOES) ............................................. 45
5.1 PILOT OVERVIEW ......................................................................................................................................... 45 5.1.1 Pilot introduction .......................................................................................................................... 45 5.1.2 Pilot overview................................................................................................................................ 45
5.2 PILOT CASE DEFINITION ................................................................................................................................. 46 5.2.1 Stakeholder and user stories ......................................................................................................... 48 5.2.2 Motivation and strategy ............................................................................................................... 49
5.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 49 5.3.1 Agriculture pilot A1.3 Motivation view ......................................................................................... 50 5.3.2 Agriculture pilot A1.3 Strategy view ............................................................................................. 50
5.4 PILOT EVALUATION PLAN .............................................................................................................................. 51 5.4.1 High level goals and KPI's ............................................................................................................. 51 5.4.2 Initial roadmap ............................................................................................................................. 51
5.5 BIG DATA ASSETS ......................................................................................................................................... 52
PILOT 4 [A2.1] BIG DATA MANAGEMENT IN GREENHOUSE ECO-SYSTEM .............................................. 54
6.1 PILOT OVERVIEW ......................................................................................................................................... 54 6.1.1 Pilot introduction .......................................................................................................................... 54 6.1.2 Pilot overview................................................................................................................................ 54
6.2 PILOT CASE DEFINITION ................................................................................................................................. 56 6.2.1 Stakeholder and user stories ......................................................................................................... 59 6.2.2 Motivation and strategy ............................................................................................................... 60
6.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 60 6.3.1 Agriculture pilot A2.1 Motivation view ......................................................................................... 60 6.3.2 Agriculture pilot A2.1 Strategy view ............................................................................................. 61
6.4 PILOT EVALUATION PLAN .............................................................................................................................. 62 6.4.1 High level goals and KPI's ............................................................................................................. 62 6.4.2 Initial roadmap ............................................................................................................................. 63
6.5 BIG DATA ASSETS ......................................................................................................................................... 64
PILOT 5 [B1.1] CEREAL, BIOMASS AND COTTON CROPS_1 .................................................................... 65
7.1 PILOT OVERVIEW ......................................................................................................................................... 65 7.1.1 Pilot introduction .......................................................................................................................... 65 7.1.2 Pilot overview................................................................................................................................ 65
7.2 PILOT CASE DEFINITION ................................................................................................................................. 68 7.2.1 Stakeholder and user stories ......................................................................................................... 68 7.2.2 Motivation and strategy ............................................................................................................... 69
7.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 69 7.3.1 Agriculture pilot B1.1 motivation view ......................................................................................... 69 7.3.2 Agriculture pilot B1.1 strategy view .............................................................................................. 70
7.4 PILOT EVALUATION PLAN .............................................................................................................................. 71 7.4.1 High level goals and KPI's ............................................................................................................. 71 7.4.2 Initial roadmap ............................................................................................................................. 72
7.5 BIG DATA ASSETS ......................................................................................................................................... 73
PILOT 6 [B1.2] CEREAL, BIOMASS AND COTTON CROPS_2 .................................................................... 74
8.1 PILOT OVERVIEW ......................................................................................................................................... 74 8.1.1 Pilot introduction .......................................................................................................................... 74 8.1.2 Pilot overview................................................................................................................................ 74
8.2 PILOT CASE DEFINITION ................................................................................................................................. 76
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8.2.1 Stakeholder and user stories ......................................................................................................... 78 8.2.2 Motivation and strategy ............................................................................................................... 78
8.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 79 8.3.1 Agriculture pilot B1.2 Motivation view ......................................................................................... 79 8.3.2 Agriculture pilot B1.2 Strategy view ............................................................................................. 80
8.4 PILOT EVALUATION PLAN .............................................................................................................................. 80 8.4.1 High level goals and KPI's ............................................................................................................. 80 8.4.2 Initial roadmap ............................................................................................................................. 80
8.5 BIG DATA ASSETS ......................................................................................................................................... 82
PILOT 7 [B1.3] CEREAL, BIOMASS AND COTTON CROPS_3 .................................................................... 83
9.1 PILOT OVERVIEW ......................................................................................................................................... 83 9.1.1 Pilot introduction .......................................................................................................................... 83 9.1.2 Pilot overview................................................................................................................................ 83
9.2 PILOT CASE DEFINITION ................................................................................................................................. 86 9.2.1 Stakeholder and user stories ......................................................................................................... 89 9.2.2 Motivation and strategy ............................................................................................................... 90
9.3 PILOT MODELLING WITH ARCHIMATE .............................................................................................................. 91 9.3.1 Agriculture pilot B1.3 Motivation view ......................................................................................... 91 9.3.2 Agriculture pilot B1.3 Strategy view ............................................................................................. 92
9.4 PILOT EVALUATION PLAN .............................................................................................................................. 92 9.4.1 High level goals and KPI's ............................................................................................................. 92 9.4.2 Initial roadmap ............................................................................................................................. 93
9.5 BIG DATA ASSETS ......................................................................................................................................... 94
PILOT 8 [B1.4] CEREAL, BIOMASS AND COTTON CROPS_4 .................................................................... 96
10.1 PILOT OVERVIEW .................................................................................................................................... 96 10.1.1 Pilot introduction...................................................................................................................... 96 10.1.2 Pilot overview ........................................................................................................................... 96
10.2 PILOT CASE DEFINITION ............................................................................................................................ 97 10.2.1 Stakeholder and user stories .................................................................................................... 99 10.2.2 Motivation and strategy ........................................................................................................ 100
10.3 PILOT MODELLING WITH ARCHIMATE ....................................................................................................... 101 10.3.1 Agriculture pilot B1.4 Motivation view .................................................................................. 101 10.3.2 Agriculture pilot B1.4 Strategy view ....................................................................................... 102
10.4 PILOT EVALUATION PLAN ....................................................................................................................... 102 10.4.1 High level goals and KPI's ....................................................................................................... 102 10.4.2 Initial roadmap ....................................................................................................................... 102
10.5 BIG DATA ASSETS .................................................................................................................................. 103
PILOT 9 [B2.1] MACHINERY MANAGEMENT ........................................................................................ 104
11.1 PILOT OVERVIEW .................................................................................................................................. 104 11.1.1 Pilot introduction.................................................................................................................... 104 11.1.2 Pilot overview ......................................................................................................................... 104
11.2 PILOT CASE DEFINITION .......................................................................................................................... 106 11.2.1 Stakeholder and user stories .................................................................................................. 109 11.2.2 Motivation and strategy ........................................................................................................ 109
11.3 PILOT MODELLING WITH ARCHIMATE ....................................................................................................... 110 11.3.1 Agriculture pilot B2.1 Motivation view ................................................................................. 110 11.3.2 Agriculture Pilot B2.1 Strategy view ....................................................................................... 111
11.4 PILOT EVALUATION PLAN ....................................................................................................................... 111 11.4.1 High level goals and KPI's ....................................................................................................... 111
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11.4.2 Initial roadmap ....................................................................................................................... 112 11.5 BIG DATA ASSETS .................................................................................................................................. 112
PILOT 10 [C1.1] INSURANCE (GREECE)................................................................................................. 113
12.1 PILOT OVERVIEW .................................................................................................................................. 113 12.1.1 Pilot introduction.................................................................................................................... 113 12.1.2 Pilot overview ......................................................................................................................... 113
12.2 PILOT CASE DEFINITION .......................................................................................................................... 115 12.2.1 Stakeholder and user stories .................................................................................................. 117 12.2.2 Motivation and strategy ........................................................................................................ 118
12.3 PILOT MODELLING WITH ARCHIMATE ....................................................................................................... 118 12.3.1 Agriculture pilot C1.1 Motivation view .................................................................................. 118 12.3.2 Agriculture C1.1 Strategy view ............................................................................................... 119
12.4 PILOT EVALUATION PLAN ....................................................................................................................... 120 12.4.1 High level goals and KPI's ....................................................................................................... 120 12.4.2 Initial roadmap ....................................................................................................................... 120
12.5 BIG DATA ASSETS .................................................................................................................................. 121
PILOT 11 [C1.2] FARM WEATHER INSURANCE ASSESSMENT ............................................................... 122
13.1 PILOT OVERVIEW .................................................................................................................................. 122 13.1.1 Pilot introduction.................................................................................................................... 122 13.1.2 Pilot overview ......................................................................................................................... 122
13.2 PILOT CASE DEFINITION .......................................................................................................................... 125 13.2.1 Stakeholder and user stories .................................................................................................. 126 13.2.2 Motivation and strategy ........................................................................................................ 126
13.3 PILOT MODELLING WITH ARCHIMATE ....................................................................................................... 127 13.3.1 Agriculture pilot C1.2 Motivation view .................................................................................. 127 13.3.2 Agriculture pilot C1.2 Strategy view ....................................................................................... 129
13.4 PILOT EVALUATION PLAN ....................................................................................................................... 130 13.4.1 High level goals and KPI's ....................................................................................................... 130 13.4.2 Initial roadmap ....................................................................................................................... 130
13.5 BIG DATA ASSETS .................................................................................................................................. 131
PILOT 12 [C2.1] CAP SUPPORT ............................................................................................................ 132
14.1 PILOT OVERVIEW .................................................................................................................................. 132 14.1.1 Pilot introduction.................................................................................................................... 132 14.1.2 Pilot overview ......................................................................................................................... 132
14.2 PILOT CASE DEFINITION .......................................................................................................................... 136 14.2.1 Stakeholder and user stories .................................................................................................. 138 14.2.2 Motivation and strategy ........................................................................................................ 138
14.3 PILOT MODELLING WITH ARCHIMATE ....................................................................................................... 138 14.3.1 Agriculture pilot C2.1 Motivation view .................................................................................. 138 14.3.2 Agriculture pilot C2.1 Strategy view ....................................................................................... 140
14.4 PILOT EVALUATION PLAN ....................................................................................................................... 141 14.4.1 High level goals and KPI's ....................................................................................................... 141 14.4.2 Initial roadmap ....................................................................................................................... 141
14.5 BIG DATA ASSETS .................................................................................................................................. 142
PILOT 13 [C.2.2] CAP SUPPORT (GREECE) ............................................................................................ 143
15.1.1 Pilot introduction.................................................................................................................... 143 15.1.2 Pilot overview ......................................................................................................................... 143
15.2 PILOT CASE DEFINITION .......................................................................................................................... 145
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15.2.1 Stakeholder and user stories .................................................................................................. 147 15.2.2 Motivation and strategy ........................................................................................................ 148
15.3 PILOT MODELLING WITH ARCHIMATE ....................................................................................................... 148 15.3.1 Agriculture pilot C2.2 Motivation view .................................................................................. 148 15.3.2 Agriculture pilot C2.2 Strategy view ....................................................................................... 149
15.4 PILOT EVALUATION PLAN ....................................................................................................................... 151 15.4.1 High level goals and KPI's ....................................................................................................... 151 15.4.2 Initial roadmap ....................................................................................................................... 151
15.5 BIG DATA ASSETS .................................................................................................................................. 152
CONCLUSION ...................................................................................................................................... 153
REFERENCES ....................................................................................................................................... 154
Table of Figures FIGURE 1: ARCHIMATE 3.0 MODELLING FRAMEWORK. ..................................................................................................... 23 FIGURE 2: RELATIONSHIPS OF THE MOTIVATION ELEMENTS ................................................................................................ 26 FIGURE 3: RELATIONSHIPS OF THE STRATEGY ELEMENTS .................................................................................................... 26 FIGURE 4: AGRICULTURE PILOT A1.1 MOTIVATION VIEW .................................................................................................. 33 FIGURE 5: AGRICULTURE PILOT A1.1 STRATEGY VIEW ....................................................................................................... 34 FIGURE 6: AGRICULTURE PILOT A1.1 INITIAL ROADMAP .................................................................................................... 35 FIGURE 7: AGRICULTURE PILOT A1.1 BDVA REFERENCE MODEL ......................................................................................... 36 FIGURE 8: AGRICULTURE PILOT A1.2 MOTIVATION VIEW .................................................................................................. 42 FIGURE 9: AGRICULTURE PILOT A1.2 STRATEGY VIEW ....................................................................................................... 42 FIGURE 10: AGRICULTURE PILOT A1.2 INITIAL ROADMAP .................................................................................................. 43 FIGURE 11: AGRICULTURE PILOT A1.2 BDVA REFERENCE MODEL ....................................................................................... 44 FIGURE 12: AGRICULTURE PILOT A1.3 MOTIVATION VIEW ................................................................................................ 50 FIGURE 13: AGRICULTURE PILOT A1.3 STRATEGY VIEW ..................................................................................................... 51 FIGURE 14: AGRICULTURE PILOT A1.3 INITIAL ROADMAP .................................................................................................. 52 FIGURE 15:AGRICULTURE PILOT A1.3 BDVA REFERENCE MODEL ....................................................................................... 53 FIGURE 16: AGRICULTURE PILOT A2.1 MOTIVATION VIEW ................................................................................................ 61 FIGURE 17: AGRICULTURE PILOT A2.1 STRATEGY VIEW ..................................................................................................... 62 FIGURE 18: AGRICULTURE PILOT A2.1 INITIAL ROADMAP .................................................................................................. 63 FIGURE 19: AGRICULTURE PILOT A2.1 BDVA REFERENCE MODEL ....................................................................................... 64 FIGURE 20: AGRICULTURE PILOT B1.1 TRAGSA MOTIVATION VIEW .................................................................................. 70 FIGURE 21: AGRICULTURE PILOT B1.1 STRATEGY VIEW ..................................................................................................... 71 FIGURE 22: AGRICULTURE PILOT B1.1 INITIAL ROADMAP .................................................................................................. 72 FIGURE 23: AGRICULTURE PILOT B1.1 BDVA REFERENCE MODEL ....................................................................................... 73 FIGURE 24: AGRICULTURE PILOT B1.2 MOTIVATION VIEW ................................................................................................ 79 FIGURE 25: AGRICULTURE PILOT B1.2 STRATEGY VIEW ..................................................................................................... 80 FIGURE 26: AGRICULTURE PILOT B1.2 INITIAL ROADMAP .................................................................................................. 81 FIGURE 27: AGRICULTURE PILOT B1.2 BDVA REFERENCE MODEL ....................................................................................... 82 FIGURE 28: AGRICULTURE PILOT B1.3 MOTIVATION VIEW ................................................................................................ 91 FIGURE 29: AGRICULTURE PILOT B1.3 STRATEGY VIEW ..................................................................................................... 92 FIGURE 30: AGRICULTURE PILOT B1.3 INITIAL ROADMAP .................................................................................................. 93 FIGURE 31: AGRICULTURE PILOT B1.3 BDVA REFERENCE MODEL FOR IOT ........................................................................... 94 FIGURE 32: AGRICULTURE PILOT B1.3 BDVA REFERENCE MODEL FOR SATELLITE DATA ........................................................... 95 FIGURE 33: AGRICULTURE PILOT B1.4 MOTIVATION VIEW .............................................................................................. 101 FIGURE 34: AGRICULTURE PILOT B1.4 STRATEGY VIEW ................................................................................................... 102 FIGURE 35: AGRICULTURE PILOT B1.4 INITIAL ROADMAP ................................................................................................ 103
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FIGURE 36: AGRICULTURE PILOT B1.4 BDVA REFERENCE MODEL ..................................................................................... 103 FIGURE 37: ZETOR TRACTORS .................................................................................................................................... 105 FIGURE 38: AGRICULTURE PILOT B2.1 MOTIVATION VIEW .............................................................................................. 110 FIGURE 39: AGRICULTURE PILOT B2.1 STRATEGY VIEW ................................................................................................... 111 FIGURE 40: AGRICULTURE PILOT B2.1 INITIAL ROADMAP ................................................................................................ 112 FIGURE 41: AGRICULTURE PILOT B2.1 STRATEGY VIEW ................................................................................................... 112 FIGURE 42: AGRICULTURE PILOT C1.1 MOTIVATION VIEW .............................................................................................. 118 FIGURE 43: AGRICULTURE PILOT C1.1 STRATEGY VIEW ................................................................................................... 119 FIGURE 44: AGRICULTURE PILOT C1.1 INITIAL ROADMAP ................................................................................................ 120 FIGURE 45: AGRICULTURE PILOT C1.1 BDVA REFERENCE MODEL ..................................................................................... 121 FIGURE 46: AGRICULTURE PILOT C1.2 MOTIVATION VIEW .............................................................................................. 128 FIGURE 47: AGRICULTURE PILOT C1.2 STRATEGY VIEW ................................................................................................... 129 FIGURE 48: AGRICULTURE PILOT C1.2 INITIAL ROADMAP ................................................................................................ 130 FIGURE 49: AGRICULTURE PILOT C1.2 BDVA REFERENCE MODEL ..................................................................................... 131 FIGURE 50: AGRICULTURE PILOT C2.1 MOTIVATION VIEW .............................................................................................. 139 FIGURE 51: AGRICULTURE PILOT C2.1 STRATEGY VIEW ................................................................................................... 140 FIGURE 52: AGRICULTURE PILOT C2.1 INITIAL ROADMAP ................................................................................................ 141 FIGURE 53: AGRICULTURE PILOT C2.1 BVDA REFERENCE MODEL ..................................................................................... 142 FIGURE 54: AGRICULTURE PILOT C2.2 MOTIVATION VIEW .............................................................................................. 149 FIGURE 55: AGRICULTURE PILOT C2.2 STRATEGY VIEW ................................................................................................... 150 FIGURE 56: AGRICULTURE PILOT C2.2 INITIAL ROADMAP ................................................................................................ 151 FIGURE 57: AGRICULTURE PILOT C2.2 BDVA REFERENCE MODEL ..................................................................................... 152
List of Tables TABLE 1: THE DATABIO CONSORTIUM PARTNERS ............................................................................................................. 13 TABLE 2: OVERVIEW OF AGRICULTURE PILOT CASES .......................................................................................................... 18 TABLE 3: ARCHIMATE MOTIVATION AND STRATEGY VIEWS................................................................................................ 23 TABLE 4: ELEMENTS USED IN THE ARCHIMATE MOTIVATION AND STRATEGY VIEWS ................................................................ 24 TABLE 5: AGRICULTURE PILOT A1.1 OVERVIEW OF PILOT ACTIVITIES .................................................................................... 27 TABLE 6: SUMMARY OF PILOT A1.1 (ISO JTC1 WG9 USE CASE TEMPLATE) ......................................................................... 29 TABLE 7: AGRICULTURE PILOT A1.1 STAKEHOLDERS AND USER STORIES................................................................................ 32 TABLE 8: SUMMARY OF PILOT A1.2 (ISO JTC1 WG9 USE CASE TEMPLATE) ......................................................................... 38 TABLE 9: AGRICULTURE PILOT A1.2 STAKEHOLDERS AND USER STORIES ................................................................................ 41 TABLE 10: SUMMARY OF PILOT A1.3 (ISO JTC1 WG9 USE CASE TEMPLATE) ....................................................................... 46 TABLE 11: AGRICULTURE PILOT A1.3 STAKEHOLDERS AND USER STORIES.............................................................................. 49 TABLE 12: SUMMARY OF PILOT A2.1 (ISO JTC1 WG9 USE CASE TEMPLATE) ....................................................................... 56 TABLE 13: AGRICULTURE PILOT A2.1 STAKEHOLDERS AND USER STORIES .............................................................................. 59 TABLE 14: SUMMARY OF PILOT B1.1 (ISO JTC1 WG9 USE CASE TEMPLATE) ....................................................................... 65 TABLE 15: AGRICULTURE PILOT B1.1 STAKEHOLDERS AND USER STORIES .............................................................................. 68 TABLE 16: AGRICULTURE PILOT B1.2 OVERVIEW OF PILOT ACTIVITIES .................................................................................. 74 TABLE 17: SUMMARY OF PILOT B1.2 (ISO JTC1 WG9 USE CASE TEMPLATE) ....................................................................... 76 TABLE 18: AGRICULTURE PILOT B1.2 STAKEHOLDERS AND USER STORIES .............................................................................. 78 TABLE 19: AGRICULTURE PILOT B1.3 OVERVIEW OF PILOT ACTIVITIES .................................................................................. 84 TABLE 20: SUMMARY OF PILOT B1.3 (ISO JTC1 WG9 USE CASE TEMPLATE) ....................................................................... 86 TABLE 21: AGRICULTURE PILOT B1.3 STAKEHOLDERS AND USER STORIES .............................................................................. 89 TABLE 22: SUMMARY OF PILOT B1.4 (ISO JTC1 WG9 USE CASE TEMPLATE) ....................................................................... 97 TABLE 23: AGRICULTURE PILOT B1.4 STAKEHOLDERS AND USER STORIES .............................................................................. 99 TABLE 24: SUMMARY OF PILOT B2.1 (ISO JTC1 WG9 USE CASE TEMPLATE) ..................................................................... 106 TABLE 25: AGRICULTURE PILOT B2.1 STAKEHOLDERS AND USER STORIES ............................................................................ 109
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TABLE 26: AGRICULTURE PILOT C1.1 OVERVIEW OF PILOT ACTIVITIES ................................................................................ 113 TABLE 27: SUMMARY OF PILOT C1.1 (ISO JTC1 WG9 USE CASE TEMPLATE)...................................................................... 115 TABLE 28: AGRICULTURE PILOT C1.1 STAKEHOLDERS AND USER STORIES ............................................................................ 117 TABLE 29: SUMMARY OF PILOT C1.2 (ISO JTC1 WG9 USE CASE TEMPLATE)...................................................................... 123 TABLE 30: AGRICULTURE PILOT C1.2 STAKEHOLDERS AND USER STORIES ............................................................................ 126 TABLE 31: SUMMARY OF PILOT C2.1 (ISO JTC1 WG9 USE CASE TEMPLATE)...................................................................... 133 TABLE 32: AGRICULTURE PILOT C2.1 OVERVIEW OF PILOT ACTIVITIES ................................................................................ 137 TABLE 33: AGRICULTURE PILOT C2.1 STAKEHOLDERS AND USER STORIES ............................................................................ 138 TABLE 34: AGRICULTURE PILOT C2.2 OVERVIEW OF PILOT ACTIVITIES ................................................................................ 144 TABLE 35: SUMMARY OF PILOT C2.2 (ISO JTC1 WG9 USE CASE TEMPLATE)...................................................................... 145 TABLE 36: AGRICULTURE PILOT C2.2 STAKEHOLDERS AND USER STORIES ............................................................................ 147
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Definitions, Acronyms and Abbreviations Acronym/
Abbreviation Title
BDVA Big Data Value Association
BDT Big Data Technology
CAP Common Agricultural Policy
CEN European Committee for Standardization
EO Earth Observation
ESA European Space Agency
EAGF European Agricultural Guarantee Fund
EU European Union
FAO Food and Agriculture Organisation of the United Nations
fAPAR fraction of Absorbed Photosynthetically Active Radiation
FAS Farm Advisory System
GAEC Good Agricultural and Environmental Conditions
GEOSS Group on Earth Observations
GPRS General Packet Radio Service
GS Genomic Selection
HPC High Performance Computing
IACS Integrated Administration and Control System
ICT Information and Communication Technologies
IoT Internet of Things
ISO International organization for Standardisation
KPI Key Performance Indicator
LPIS Land Parcel Identification System
NDVI Normalized Difference Vegetation Index
NGS Next-Generation Sequencing
NUTS Nomenclature of Territorial Units for Statistic
PC Personal Computer
PF Precision Farming
PU Public
RPAS Remotely Piloted Aircraft System
RTK Real Time Kinematic
SMEs Small and medium-sized enterprises
TRL Technology Readiness Level
UAV Unmanned Aerial Vehicle
UI User Interface
UVA, UVB (UV) ultraviolet rays, (A) long wave, (B) short wave
VRA Variable Rate Application
WP Work Package
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Term Definition
Big Data A term of data sets that are so large or complex that traditional data
processing application software is inadequate to dealing with them
In situ Latin phrase translated “on site” or “on position”- it means “locally” or “in
place” to describe an event where it takes place
NDVI A simple graphical indicator that can be used to analyse remote sensing
measurements
WP (Work
Package)
A building block of the work breakdown structure that allows the project
management to define the steps necessary for completion of the work
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Introduction 1.1 Project Summary The data intensive target sector on which the
DataBio project focuses is the Data-Driven
Bioeconomy. DataBio focuses on utilizing Big
Data to contribute to the production of the
best possible raw materials from agriculture,
forestry and fishery (aquaculture) for the
bioeconomy industry, as well as their further
processing into food, energy and
biomaterials, while taking into account various accountability and sustainability issues.
DataBio will deploy state-of-the-art big data technologies and existing partners’ infrastructure
and solutions, linked together through the DataBio Platform. These will aggregate Big Data
from the three identified sectors (agriculture, forestry and fishery), intelligently process them
and allow the three sectors to selectively utilize numerous platform components, according
to their requirements. The execution will be through continuous cooperation of end user and
technology provider companies, bioeconomy and technology research institutes, and
stakeholders from the big data value PPP programme.
DataBio is driven by the development, use and evaluation of a large number of pilots in the
three identified sectors, where associated partners and additional stakeholders are also
involved. The selected pilot concepts will be transformed to pilot implementations utilizing
co-innovative methods and tools. The pilots select and utilize the best suitable market-ready
or almost market-ready ICT, Big Data and Earth Observation methods, technologies, tools and
services to be integrated to the common DataBio Platform.
Based on the pilot results and the new DataBio Platform, new solutions and new business
opportunities are expected to emerge. DataBio will organize a series of trainings and
hackathons to support its uptake and to enable developers outside the consortium to design
and develop new tools, services and applications based on and for the DataBio Platform.
The DataBio consortium is listed in Table 1. For more information about the project see [REF-
01].
Table 1: The DataBio consortium partners
Number Name Short name Country
1 (CO) INTRASOFT INTERNATIONAL SA INTRASOFT Belgium
2 LESPROJEKT SLUZBY SRO LESPRO Czech Republic
3 ZAPADOCESKA UNIVERZITA V PLZNI UWB Czech Republic
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4
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER
ANGEWANDTEN FORSCHUNG E.V. Fraunhofer Germany
5 ATOS SPAIN SA ATOS Spain
6 STIFTELSEN SINTEF SINTEF ICT Norway
7 SPACEBEL SA SPACEBEL Belgium
8
VLAAMSE INSTELLING VOOR TECHNOLOGISCH
ONDERZOEK N.V. VITO Belgium
9
INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ
AKADEMII NAUK PSNC Poland
10 CIAOTECH Srl CiaoT Italy
11 EMPRESA DE TRANSFORMACION AGRARIA SA TRAGSA Spain
12 INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EV INFAI Germany
13 NEUROPUBLIC AE PLIROFORIKIS & EPIKOINONION NP Greece
14
Ústav pro hospodářskou úpravu lesů Brandýs nad
Labem UHUL FMI Czech Republic
15 INNOVATION ENGINEERING SRL InnoE Italy
16 Teknologian tutkimuskeskus VTT Oy VTT Finland
17 SINTEF FISKERI OG HAVBRUK AS
SINTEF
Fishery Norway
18 SUOMEN METSAKESKUS-FINLANDS SKOGSCENTRAL METSAK Finland
19 IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD IBM Israel
20 MHG SYSTEMS OY - MHGS MHGS Finland
21 NB ADVIES BV NB Advies Netherlands
22
CONSIGLIO PER LA RICERCA IN AGRICOLTURA E
L'ANALISI DELL'ECONOMIA AGRARIA CREA Italy
23 FUNDACION AZTI - AZTI FUNDAZIOA AZTI Spain
24 KINGS BAY AS KingsBay Norway
25 EROS AS Eros Norway
26 ERVIK & SAEVIK AS ESAS Norway
27 LIEGRUPPEN FISKERI AS LiegFi Norway
28 E-GEOS SPA e-geos Italy
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29 DANMARKS TEKNISKE UNIVERSITET DTU Denmark
30 FEDERUNACOMA SRL UNIPERSONALE Federu Italy
31
CSEM CENTRE SUISSE D'ELECTRONIQUE ET DE
MICROTECHNIQUE SA - RECHERCHE ET
DEVELOPPEMENT CSEM Switzerland
32 UNIVERSITAET ST. GALLEN UStG Switzerland
33 NORGES SILDESALGSLAG SA Sildes Norway
34 EXUS SOFTWARE LTD EXUS
United
Kingdom
35 CYBERNETICA AS CYBER Estonia
36
GAIA EPICHEIREIN ANONYMI ETAIREIA PSIFIAKON
YPIRESION GAIA Greece
37 SOFTEAM Softeam France
38
FUNDACION CITOLIVA, CENTRO DE INNOVACION Y
TECNOLOGIA DEL OLIVAR Y DEL ACEITE CITOLIVA Spain
39 TERRASIGNA SRL TerraS Romania
40
ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS
ANAPTYXIS CERTH Greece
41
METEOROLOGICAL AND ENVIRONMENTAL EARTH
OBSERVATION SRL MEEO Italy
42 ECHEBASTAR FLEET SOCIEDAD LIMITADA ECHEBF Spain
43 NOVAMONT SPA Novam Italy
44 SENOP OY Senop Finland
45
UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO
UNIBERTSITATEA EHU/UPV Spain
46
OPEN GEOSPATIAL CONSORTIUM (EUROPE) LIMITED
LBG OGCE
United
Kingdom
47 ZETOR TRACTORS AS ZETOR Czech Republic
48
COOPERATIVA AGRICOLA CESENATE SOCIETA
COOPERATIVA AGRICOLA CAC Italy
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1.2 Document Scope Deliverable D1.1 – Agriculture Pilot Definition (due M06) specifies the pilot case descriptions,
requirement specifications, and implementation and evaluation plans. The document
describes 13 pilots and it will serve as basis for implementation of agriculture pilots, which
will be described in Agriculture Pilots intermediate report - Pilot results and feedback from
users in Month 24.
1.3 Document Structure
This document is comprised of the following chapters:
Chapter 1 presents an introduction to the project and the document.
Chapter 2 gives a general overview of the Agriculture Pilots t and summarises key points of
the pilot cases.
Chapters 3 to 17 describe the individual pilot cases.
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Summary 2.1 Overview The agriculture sector is of strategic importance for the European society and economy. Due
to its complexity, agri-food operators have to manage many different and heterogeneous
sources of information. Agriculture is facing many economic challenges in terms of
productivity or cost-effectiveness, as well as an increasing labour shortage partly due to
depopulation of rural areas. Current systems still have significant drawbacks in areas such as
flexibility, efficiency, robustness, sustainability, high operator cost and capital investment.
Furthermore, reliable detection, accurate identification and proper quantification of
pathogens and other factors, affecting plant health, common agriculture policy, insurance,
are critical to be kept under control so as to reduce economy expenditures, trade disruptions
and even human health risks. Agriculture requires collection, storage, sharing and analysis of
large quantities of spatially and non-spatially referenced data. These data flows currently
hinder the adoption of precision agriculture as the multitude of data models, formats,
interfaces and reference systems in use result in incompatibilities. In order to plan and make
economically and environmentally sound decisions a combination and management of
information is needed.
2.2 Pilot introductions Big data technology (BDT) is a new technological paradigm that is driving the entire economy,
including low-tech industries such as agriculture where it is implemented under the banner
of precision farming (PF) [REF-03]. BDT in agriculture builds on geo-coded maps of agricultural
fields and the real-time monitoring of activities on the farm in order to increase the efficiency
of resource use, reduce the uncertainty of management decisions [REF-04]. Under PF, yield is
increased due particularly to the precise selection and application of exact types and doses of
agricultural inputs (crop varieties, fertilizers, pesticides, herbicides, irrigation water) for
optimum crop growth and development.
In terms of technology readiness level (TRL), the agriculture pilots are mostly positioned at
the sixth and seventh TRL. Improved technologies such as new elite varieties were developed,
big data such as weather, soil, crop (phenotypic data), and other environmental data are
routinely collected and meta-analysed, and technological and managerial services are already
offered to farmers in a few nations for a number of crops, although not in a scale that would
enable the application of big data analytics. There also exist experiences with farm telemetry
or utilization of satellite data (Earth Observation) in some countries. In addition, the required
skills are available in the organizations participating in the pilots, and the organizations are
ready to change their internal and external business processes, which is a key factor for
adopting the new technology.
The European farming system represents a mixture of small and big farms [REF-05]. In order
for WP1 pilots to account for both small and bigger farms, agriculture data serving as an input
into the big data analytics system will be gathered on a finer and a larger scale. The finer scale
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is tailored to both farm sizes but with a particular focus on bigger farms with more financial
resources. Finer scale data include (1) data collected manually on soil, plants and other
agriculturally relevant factors, and through surveys and interviews; (2) historic big agriculture
and meteorological datasets; and (3) field-bound wireless sensor networks. Larger scale data
will be mainly derived from earth observation (EO) and include agriculturally relevant
information collected using remote sensing technologies and earth surveying techniques, and
from data coming from agriculture machinery. EO and finer scale information will be used
through big data analytics (WP4) to monitor and assess the status of, and changes in, the
agriculture pilots implemented in this project all across the European Union. Big data analytics
components and tools will then provide pilot managers with highly localized descriptive
(better and more advanced way of analysing an operation), prescriptive (timely
recommendations for operation improvement i.e., seed, fertilizer and other agricultural
inputs application rates, soil analysis, and localized weather and disease/pest reports, based
on real-time and historical data), and predictive plans (use current and historical data sets to
forecast future localized events and returns).
2.3 Overview of pilot cases The agriculture pilot cases are divided into three main topics as shown in the table below. For
all the pilots, co-innovative requirements (Task 1.1) were defined within the first six months
(M1-M6) of the project. Pilots activities under real production environment conditions will be
run over two to three cropping seasons (M6-M34) depending upon the plant species of
interest. (Tasks 1.2, 1.3, 1.4)
Table 2: Overview of agriculture pilot cases
Task (topic) Subtask Pilot group Pilot
T1.2 (A) Precision
Horticulture including
vine and olives
T1.2.1 A1: Precision agriculture
in olives, fruits, grapes
and vegetables
A1.1: Precision
agriculture in olives,
fruits, grapes
A1.2: Precision
agriculture in vegetable
seed crops
A1.3: Precision
agriculture in vegetables
-2 (Potatoes)
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T1.2.2 A2: Big Data management
in greenhouse eco-
systems
A2.1: Big Data
management in
greenhouse eco-
systems
T1.3 (B) Arable
Precision Farming
T1.3.1 B1: Cereal, biomass and
cotton crops
B1.1: Cereal, biomass
and cotton crops 1
B1.2: Cereal, biomass
and cotton crops 2
B1.3: Cereal, biomass
and cotton crops 3
B1.4: Cereal, biomass
and cotton crops 4
T1.3.2 B2: Machinery
management
B2.1: Machinery
management
T1.4 (C) Subsidies and
insurance
T1.4.1 C1: Insurance C1.1: Insurance (Greece)
C1.2: Farm Weather
Insurance Assessment
T1.4.2 C2: CAP support C2.1: CAP Support
C2.2: CAP Support
(Greece)
The topics are defined as follows:
A. Precision Horticulture including vine and olives led by NP: In our days, farmers face a
series of challenges in their business. Resistant crop diseases and climate change
affects their crop production. At the same time, as the global demand for commodities
increases, farmers are forced to maximize their production. Following the rules of the
modern agro-food market, farmers and cooperatives that wish to export their
products abroad, need to follow smart agriculture practices.
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B. Arable Precision Farming led by Vito: The overall objective is to implement big data
technology tools for precision and resilient farming of the food crop species of interest
including durum wheat, corn, grapes, etc. Focus of this pilot will be not only on
production aspects, but also on protection of water and soil as well as on energy
saving.
C. CAP support and insurance lead institution led by e-GEOS: The focus will be on using
Earth Observation data for the purpose of insurance and EU Common Agricultural
Policy.
Each topic includes two pilot groups:
Pilot group A1: Precision agriculture in olives, fruits, grapes and vegetables (NEUROPUBLIC,
VITO, GAIA, InfAI and CAC)
The following services will be offered:
• Remote plant disease diagnosis and assessment based on the processing of Satellite images;
• weather condition alert system which will result in the decision taking of specific actions; (e.g., crop protection);
• provision of automated irrigation systems based in precision irrigation enabling in this way an efficient water resource management system;
• support of efficient soil fertilization and spray practices consistent with the specific needs of the farm and the protection of the environment;
• advisory services regarding crop diversification will be also provided to the farmers directing them in more productive and resilient cultivations.
It will be focused on combined use of soil data, weather data, map data, satellite (LR, HR, VHR,
SAR), farm logs, UAV, farm profile data, and data collected by mobile audio-visual devices.
Pilot group A2: Big Data management in greenhouse eco-systems (CERTH, CREA)
The overall objective of the proposed pilot is to provide knowledge, know‐how & tools related
to the information flow, management and data analytics in greenhouse horticulture. To this
purpose, genomics, metabolomics and phenomics data will be combined. During this project,
it will be used already produced genomic data which will be integrated with new ones in order
to assess the genetic potential of new tomato varieties and their performance in greenhouses.
The aim is to integrate metabolomics and genomics data to obtain a complete identity of the
varieties for breeding applications. Liquid chromatography - mass spectrometry (LC-MS), Gas
chromatography - mass spectrometry (GS-MS), High-performance liquid chromatography
(HPLC) will be used to collect the metabolomics data. Market potential and industry interests:
Tomato is among the top cultivated crops in greenhouses, with billions of euros turnover
worldwide. Tomato is considered one of the most nutritive solanum vegetables due to its high
content in sugars, vitamins and antioxidants and its consumption is steadily increasing. The
pilot is expected to leverage the productivity and the quality of tomato.
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Pilot group B1: Cereal, biomass and cotton crops (Vito, Lesprojekt, NEUROPUBLIC,
Federunacoma, CREA, NOVAMONT, ZETOR, GAIA, CERTH, NB Advies, CiaoT, ASTER, InfAI,
Lesprojekt, Federunacoma, e-GEOS, PSNC, TRAGSA)
This pilot aims to provide information for precision agriculture, mainly based on time series
of high resolution (Sentinel-2 type) satellite images, complemented with UAV images, metro
and field (sensor) data. The information can be used as input for farm management
(operational decisions, tactical decisions). Information layers may include: - Vegetation
indices (NDVI, fAPAR, …) and derived anomaly maps. Anomaly maps can be used to set
priorities for field visits (local/regional level). Pilots on durum wheat will be conducted in
different environments in Italy in collaboration with Horta Srl, (private company), CNR-Ibimet
(public research institute) and local Producer organization and cooperatives in Italy using in
addition to the tools listed above. Pilots on precision irrigation in cotton will be conducted in
a NEUROPUBLIC pilot site in Greece. The pilots will run in partnership with end users GAIA
EPICHEIREIN and the local group of cotton farmers. Biomass crops (CREA, VITO, CERTH, NB
Advies, CiaoT, ASTER, InfAI, Lesprojekt, Federunacoma, e-GEOS, PSNC, TRAGSA). Biomass
crops including biomass sorghum, fiber hemp and milk thistle can be used for several
purposes including, respectively, biofuel, fiber, and biochemicals, with a high macroeconomic
impact. The pilots on these crops will be run in collaboration between CREA and private
companies (end-users) Cooperativa Agricola Cesenate (seed company), Novamont (Bio-based
company), and Centro Ricerche Produzioni Animali, and another 15 agricultural firms
distributed across the Italian territory.
Pilot group B2: Machinery management (Lesprojekt, Federunacoma, ZETOR)
From technical point of view the monitoring system involves tracking of the vehicles’ position
using GPS combined with acquisition of information from on-board terminal (CAN-BUS) and
their online or offline transfer to GIS environment. Such systems collect large amounts of
data. The monitoring system will be done in large, medium-sized and small farms based on
the level of information processing and their interaction with other farm data, three use cases
will be handled.
Pilot group C1: Insurance (e-GEOS, VITO, NEUROPUBLIC, NB Advies, CSEM)
The objective of this pilot is the provision and assessment on a test area of services for
agriculture insurance market, based on the usage of Copernicus satellite data series also
integrated with meteorological data, and other ground available data.
Pilot group C2: CAP support (e-GEOS, CSEM, NEUROPUBLIC, GAIA)
The objective of the pilot is the provision of products and services, based on specialized highly
automated processors processing big data, in support to the CAP and relying on multi-
temporal series of free and open EO data, with focus on Copernicus Sentinel 2 data. Products
and services will be tuned in order to fulfil requirements from the 2015-20 EU CAP policy, and
will be general information layers and indicators on EU territory with different level of
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aggregation and detail up to farm level. The proposed pilot project has been tailored on the
specific needs of three end users, one operating at National level (Romania Agriculture
Ministry), one operating at Regional level (AVEPA Paying Agency) in one of the most
important agricultural regions in Italy, and one operating in Greece.
2.4 Agriculture datasets utilized in pilots The datasets used by the agriculture pilots can be coarsely divided into four distinct
categories. In situ measurements are data obtained by sensors in the field, Machinery
Measurements are coming from sensors in agriculture machinery. Remote measurements are
measurements which may cover a greater geographical area, such as measurements from
satellites. VGI data and data collected by farmers. The biggest data sets will come from Earth
Observation and Machine monitoring. The current experience from Czech Republic
demonstrate that machinery monitoring in Czech Republic is yearly able to generate more
than 20 TB of data and the needs of satellite data is approximately 5 TB per year. The data
from unmanned aerial vehicles (UAV) will be much larger.
2.5 Representation of pilot cases Each pilot is described in following structure:
● PILOT OVERVIEW o Pilot introduction
o Pilot overview
● PILOT CASE DEFINITION o Stakeholder and user stories
o Motivation and strategy
● PILOT MODELLING WITH ARCHIMATE o Motivation view
o Strategy view
● PILOT EVALUATION PLAN o High level goals and KPI's
o Initial roadmap
● BIG DATA ASSETS
2.6 Pilot modelling framework The pilot cases are modelled using the ArchiMate 3.0 modelling framework. Figure 1
summarizes the overall ArchiMate 3.0 framework. The figure also depicts the input provided
by the domain WPs (WP1, WP2, WP3 and their pilots) and that provided by the technology
WPs (WP4, WP5), which will be correlated in the next stages of modelling process.
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Figure 1: ArchiMate 3.0 modelling framework.
The modelling presented in this deliverable focuses on the “Motivation” and “Strategy” views.
The “Motivation” view models the reasons that guide the design of the architecture. The
“Strategy” view adds how the course of action is realized. Table 3 provides an extended
description of the two views. After the completion of this deliverable, the plan is to extend
the modelling with other views, while investigating the correlations with the technology WP
input.
Table 3: ArchiMate Motivation and Strategy views.
View name Description
Motivation
view
Motivation elements are used to model the motivations, or reasons, that guide the
design or change of an Enterprise Architecture. It is essential to understand the
factors, often referred to as drivers, which influence other motivation elements.
They can originate from either inside or outside the enterprise. Internal drivers, also
called concerns, are associated with stakeholders, which can be some individual
human being or some group of human beings, such as a project team, enterprise, or
society. Examples of such internal drivers are customer satisfaction, compliance to
legislation, or profitability.
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Strategy
view
The immediate decision support system is built on top of a data collection and
distribution system. The data collection and distribution system is used to collect
sensor data from the on-board systems and makes them available in a single system.
The data distribution system ensures that the decision support system only interface
with a single system, instead of multiple sensors. The decision support system
presents the data from the data distribution system and collect them in an internal
storage system for presentation of current performance vs. historic performance.
The main elements used in the above views are explained in Table 4. Their relationships are
shown in Figure 2and Figure 3. For further information see [REF-02].
Table 4: Elements used in the ArchiMate Motivation and Strategy views
Element Definition Notation
Stakeholder The role of an individual, team,
or organization (or classes
thereof) that represents their
interests in the outcome of the
architecture.
Driver An external or internal condition
that motivates an organization
to define its goals and
implement the changes
necessary to achieve them.
Assessment The result of an analysis of the
state of affairs of the enterprise
with respect to some driver.
Goal A high-level statement of intent,
direction, or desired end state
for an organization and its
stakeholders.
Outcome An end result that has been
achieved.
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Principle A qualitative statement of intent
that should be met by the
architecture.
Requirement A statement of need that must
be met by the architecture.
Constraint A factor that prevents or
obstructs the realization of
goals.
Meaning The knowledge or expertise
present in, or the interpretation
given to, a core element in a
particular context.
Value The relative worth, utility, or
importance of a core element or
an outcome.
Resource An asset owned or controlled by
an individual or organization.
Capability An ability that an active
structure element, such as an
organization, person, or system,
possesses.
Course of
action
An approach or plan for
configuring some capabilities
and resources of the enterprise,
undertaken to achieve a goal.
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Figure 2: Relationships of the Motivation elements
Figure 3: Relationships of the Strategy elements
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Pilot 1 [A1.1] Precision agriculture in olives,
fruits, grapes 3.1 Pilot overview
3.1.1 Pilot introduction
The world population is expected to reach 9 billion by 2050 and feeding that population will
require a 70 percent increase in food production (FAO 2009) [REF-06]. At the same time,
farmers are facing a series of challenges in their businesses that affect their farm production,
such as crop pests and diseases with increased resistance along with drastic changes due to
the effects of the climate change. These factors lead to rising food prices that have pushed
over 40 million people into poverty since 2010, a fact that highlights the need for more
effective interventions in agriculture (World Bank 2011) [REF-07]. In this context, agri-food
researchers are working on approaches that aim at maximizing agricultural production and
reducing yield risk. The benefits of the ICT-based revolution have already significantly
improved agricultural productivity; however, there is a demonstrable need for a new
revolution that will contribute to “smart” farming and help addressing all the aforementioned
problems (World Bank 2011) [REF-07].
There is a need for services that are powered by scientific knowledge, driven by facts and
offer inexpensive yet valuable advice to farmers. In this context, smart farming is expected to
reduce production costs, increase production (quantitatively) and improve its quality, protect
the environment and minimize farmers’ risks.
3.1.2 Pilot overview
The main focus of this pilot is to offer smart farming services dedicated for olives, fruits and
grapes, based on a set of complementary monitoring technologies. Smart farming services
comprise irrigation, fertilization and pest/disease management advice provided through
flexible mechanisms and UIs (web, mobile, tablet compatible). The pilot will target towards
promoting the adoption of technological advances (IoT, Big Data analytics, EO data) and
collaborating with certified professionals to optimize farm management procedures. NP and
GAIA Epicheirein will support the activities for the execution of the full life-cycle of the pilot.
The following table provides an overview of the pilot activities.
Table 5: Agriculture pilot A1.1 Overview of pilot activities
Pilot Site A Pilot Site B Pilot Site C
Location Chalkidiki, Greece Stimagka, Greece Veria, Greece
Area Size 600ha 3,000ha 10,000ha
Targeted Crops Olive Trees Grapes Peaches
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End-Users Single farmer,
Agronomists
Farming
organization,
Agronomists
Farming
cooperative,
Agronomists
The underlying reason for the selection of these particular crop types is the significant
economic impact that they share in the Greek farming landscape. Olive tree cultivation
accounts for nearly 2 billion euros in annual net income, while peach and grape cultivations
reach close to 460 million and 390 million annual net income respectively.
Method
This pilot is targeting towards providing a set of smart farming services to the farmer utilizing
available precision agriculture techniques. The services will be provided as advices, which
need many prerequisites and primary material in order to be accurate. Data is the raw
material and there are three different means of collecting data, which will be exploited within
the pilot activities. Data directly from the field, collected from a network of telemetric IoT
stations called GAIAtrons; remotely with image sensors on in-orbit platforms; and by
monitoring the application of inputs and outputs in the farm (e.g. in-situ measurements, farm
logs, farm profile). Every data source has unique characteristics with relevant impact on the
very content of this data. Field sensing provides real-time accurate direct measures of many
physical parameters of the soil (soil temperature, humidity), atmosphere microclimate of the
field crop and plant (ambient temperature, humidity, barometric pressure, solar radiation,
leaf wetness, rainfall volume, wind speed and direction) with temporal continuity. Remote
sensing provides indirect measures of some physical properties of plants and soil with spatial
continuity in medium to large spatial scale. Combining this information can provide a good
knowledge of the most important physical parameters of soil, microclimate, plants and water
(which are all the environmental resources, which govern farming) in both spatial and
temporal dimensions. Monitoring the application of inputs and outputs on the farm is a data
element that is necessary to assess the correctness of the given advice and use it as feedback
to improve the system over time. This pilot will combine advanced data handling techniques
(i.e. assimilation, fusion and spatio-temporal interpolation) to transform the collected data
into actionable advice. In order for this advice to reflect the actual situation at a given field,
we will deploy scientific models and we will seek to incorporate the human experience of the
farmer or certified advisors.
Relevance to and availability of Big Data and Big Data infrastructure
NP has already started collecting field-sensing data through its network of telemetric IoT
stations, called GAIAtrons. GAIAtrons offer configurable data collection and transmission
rates. Since 01/03/2016 over 1M samples have been collected and stored to NP’s cloud
infrastructure that refer to atmospheric and soil measurements from various agricultural
areas of Greece. Moreover, within the same cloud infrastructure (GAIA cloud), remote sensing
data from the new Sentinel 2 optical products are being extracted and stored since the
beginning of 2016. This comprises both raw and processed (corrected products, extracted
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indices) data represented in raster formats that are being handled and distributed using
optimal big data management methodologies. Finally, through flexible work calendars, NP
has collected more than 120000 records related to work plans of the farmers that can be used
in the context of the pilot activities.
Benefit of pilot
The pilot is expected to have a direct impact on farm profitability in three (3) major crop types
of Greece, from an economic perspective. This will ensure that the proposed solutions can be
replicated to other crop types and market segments in the near future. The holistic approach
that is being proposed will significantly improve the capacity of the responsible partners in
providing smart farming advisory services. In addition, it would lead to improvements in a)
NP’s GAIA cloud’s stability, availability, security, interoperability and overall maturity, b) NP’s
GAIABus DataSmart functionality in terms of real-time analytics, data stream and decision
support processes, multi-temporal object-based monitoring, cloud-based services that
integrate earth observation with image processing, machine learning and spatial modelling,
c) advancing the current system by fusing telemetry IoT stations’ data with remote sensing
data and incorporating advanced visualization and event-based capabilities.
3.2 Pilot case definition
Table 6: Summary of pilot A1.1 (ISO JTC1 WG9 use case template)
Use case title Precision agriculture in olives, fruits, grapes
Vertical (area) Agriculture
Author/company/email NP, GAIA Epicheirein
Actors/stakeholders and their roles and
responsibilities
● Single Farmer/Farming Organization or Cooperative, responsible for performing farming activities
● Agronomists, involved in providing relevant and up-to-date advices to the farmers
Goals Provide smart farming advisory services (focusing on irrigation, fertilization and pest/disease management), based on a set of complementary monitoring technologies, in order to increase farm profitability and promote sustainable farming practises.
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
Current
solutions
Compute(System) System is based on IoT data, farm logs, work calendars and in-situ measurements. Expert knowledge is provided through static scientific
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models that offer insight about optimal farm management.
Storage All available data are stored in a cloud infrastructure.
Networking Web-based UIs and dashboards available for monitoring farm activities.
Software Real-time analytics, data stream processes and decision support system
Big data
characteristics
Data source (distributed/centralized)
Centralized (within GAIA Cloud): Field sensing data from GAIAtrons, Remote sensing (Earth observation) data, Farm data
Volume (size) ● ~5.5 TB/year for remote sensing data, including raw data and extracted biophysical and vegetation indices for the pilot areas
● several GBs/year field sensing data collected by the deployed GAIAtrons (related to the number of GAIAtrons to be used within the pilot activities)
● Hundreds of thousands of records related to farm activities/profiles/measurements
Velocity
(e.g. real time)
Configurable data transmission for field sensing (a new set of measurements is being sent every 10 minutes in present configuration). Every 10 days new EO products available. Within 2018 EO products will be available every 5 days.
Variety
(multiple datasets, mashup)
Field Sensing: Soil temperature, humidity (multi-depth), ambient temperature, humidity, barometric pressure, solar radiation, leaf wetness,
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rainfall volume, wind speed and direction
Remote Sensing: 13 spectral bands
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Need for a system that can constantly provide relevant and up-to-date advices to its end-users
Visualization Spatio-temporal information visualization for improving farm management and facilitating the decision-making process
Data quality (syntax) The quality of field sensing data is controlled by several filtering, outlier detection and stream processing mechanisms. The integrity of remote sensing data quality is being assessed by a hash check upon product download.
Data types Remote sensing data provided in raster format (.jp2). Field sensing data provided as time series unstructured data with configurable frequency
Data analytics Descriptive and prescriptive analytics for the provision of irrigation, fertilization and pest management advices.
Big data specific challenges (Gaps)
There is a need for smarter fusion of the heterogeneous data types that are being collected towards providing accurate insights. To this end, it is important to explore mechanisms that could combine raster and vector data at parcel level (polygon) and station level (point).
Big data specific challenges in bio-
economy
In order to facilitate the adoption of the big data technologies by the farmers, imposed barriers in data visualization should be encountered (e.g. give more emphasis to vector data, improvement of the aggregation mechanism (drill down, zoom in, roll up, zoom out)).
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Security and privacy
technical considerations
A system intended to collect data from field sensors, installed in remote locations, is definitely going to face network connectivity challenges. In order to provide up-to-date and relevant advices, the system should be able to exhibit high availability and accuracy in its sensor readings and transmission mechanisms. Moreover, field sensing data should be securely transmitted to the cloud infrastructure and protected against various types of attacks that might set the system at risk.
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
EO data management mechanisms can be exploited for other use cases where EO data might provide valuable insights.
3.2.1 Stakeholder and user stories
Table 7: Agriculture pilot A1.1 Stakeholders and user stories
Stakeholders User story Motivation
Farmer As a farmer I want to reduce costs and improve farm productivity
Increase my profits following sustainable agriculture practices
Agronomists As an agronomist I want to have a comparative advantage in a highly competitive market and to offer the best possible services to my clients
Increase my profits by providing better advices based on evidences, well-established arguments and scientific knowledge.
3.2.2 Motivation and strategy
The main motivation for this pilot is:
• to raise the awareness of the farmers, agronomists, agricultural advisors, farmer cooperatives and organizations (e.g. group of producers) on how new technological tools could optimize farm profitability and offer a significant advantage on a highly competitive sector.
• to promote sustainable farming practises over a better control and management of the resources (water, fertilizers, etc.).
• to increase the technological capacity of the involved partners through a set of pilot activities that involves management of big data for high value crops.
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
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3.3 Pilot modelling with ArchiMate The current section presents the "Agriculture A.1.1 modelling with ArchiMate" view point
described using the ArchiMate standard.
3.3.1 Agriculture pilot A1.1 Motivation view
This section provides the "Agriculture A1.1 Motivation view" view defined in the "Agriculture
A.1.1 modelling with ArchiMate" view point.
Figure 4: Agriculture pilot A1.1 Motivation view
Farmers want cost reduction and improved productivity in order to increase their profits
following sustainable agriculture practices.
3.3.2 Agriculture pilot A1.1 Strategy view
This section provides the "Agriculture A1.1 Strategy view" view defined in the "Agriculture
A.1.1 modelling with ArchiMate" view point.
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Figure 5: Agriculture pilot A1.1 Strategy view
The main focus of this pilot is to offer smart farming services dedicated for olives, fruits and
grapes, based on a set of complementary monitoring technologies. Smart farming services
comprise irrigation, fertilization and pest/disease management advice provided through
flexible mechanisms and UIs (web, mobile, tablet compatible). The pilot will target towards
promoting the adoption of technological tools (IoT, Big Data analytics, EO data) and
collaborating with certified professionals to boost/optimize farm productivity.
3.4 Pilot Evaluation Plan
3.4.1 High level goals and KPI's
Two relevant KPIs have been identified so far, namely:
• %Reduction potential in operational costs for performing the same farming activities (through better management of resources) following the advisory irrigation, fertilization, pest/disease management services vs what would be the operational costs following standard farming practices based on historical data: Quantify %reduction potential in operational costs for all three crop types (in fresh water/fertilizer usage, sprays following the aforementioned advisory services).
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• %Increase in farm yield following the advisory irrigation, fertilization, pest/ disease management services vs what would be the yield following standard farming practices based on historical data: Quantify %increase in farm yield for all three crop types.
3.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 (WP4).
Figure 6: Agriculture pilot A1.1 initial roadmap
3.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.
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Figure 7: Agriculture pilot A1.1 BDVA reference model
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Pilot 2 [A1.2] Precision agriculture in
vegetable seed crops 4.1 Pilot overview
4.1.1 Pilot introduction
Eastern Italy is by tradition one of the areas in the world where seed production is at its best.
Seed Companies from all over the world produce on contract with local growers’ vegetables,
sugar beets, alfa-alfa and many other species.
One of the key factor for the achievement of seeds of good quality depends on the choice of
the right time of harvesting: if too early the vigour of the seed harvested will be affected; if
too late the mature seeds are going to drop to the ground and the best part of harvest get
lost.
The pilot will concentrate its main focus in monitoring the maturity of seed crops of different
species with satellite imagery. There will be an on-land observation of the crop development
which will be matched with satellite images in order to check the possibility to establish a
correspondence between images and the maturity stage of each crop.
In first growing season, the crop monitored will be sugar beet for seed production, with the
aim to expand the observation to other seed crops.
4.1.2 Pilot overview
Location: 5 farms, Region Emilia Romagna, for the total acreage of 14,79 hectares in the first
year.
To be expanded to other crops in the same Region and in Region Marche.
Method
This pilot will use satellite imagery (Sentinel-2) and telemetry IoT for crop monitoring and
yield/seed maturity estimation. The pilots will be run by C.A.C. in collaboration with VITO.
The crop involved in first year is sugar beet; according to the results achieved the model may
be expanded to other seed crops, namely cabbage and onion. VITO will use satellite data to
monitor the crops and will develop yield/seed maturity models. Telemetry IoT technology
will be implemented by C.A.C. on 5 farms located in Emilia Romagna and Marche.
Specifically, as part of pilot innovative solution, an online platform will be used to provide
satellite imagery, weather and soil data and yield/seed maturity predictions. VITO, in
collaboration with a number of Belgian partners, has developed a web application
“WatchITgrow®” for potato monitoring and yield prediction in Belgium. The existing
WatchITgrow® application will “filled” with satellite, weather and soil data for the Italian pilot
sites. To be able to provide maturity estimates developments are needed and it is necessary
to collect field data. The data will be collected by C.A.C.
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The farmer and pilot owners can use the satellite imagery (biomass index, 10m resolution) to
monitor and benchmark the maturity curve of seed crops till harvesting in correlation with
weather and microclimatic conditions recorded on site through dedicated meteorological
units.
A weather station will be installed in the vicinity of each field with sensors for air moisture
and temperature, soil temperature, rainfall – remote monitored.
Telemetry IoT stations will transmit data to the cloud infrastructure in the process of crop
monitoring, biotic and abiotic stress diagnostic, alert and operational recommendations.
Benefit of pilot
The solution that will be developed will be for the benefit of the co-operative which is
organising the production with its associated growers.
Each crop gets in maturity stage according to the cycle of the variety, microclimate, land
conditions, water supply etc.
The aim is to monitor the stage of maturity of each crop using satellite imagery (and possibly
telemetry IoT). This information can help fieldsmen to organise efficiently their time in
assisting the growers.
The fieldsman and the farmers who are participating in the pilot will have access to satellite
images, weather and soil data and information on seed maturity via an online platform. The
farmers will provide crop data about their fields for system learning.
4.2 Pilot case definition
Table 8: Summary of pilot A1.2 (ISO JTC1 WG9 use case template)
Use case title Precision agriculture in seed crops
Vertical (area) Agriculture
Author/company/email Stefano Balestri / C.A.C. / balestriacseeds.it
Isabelle Piccard / VITO
Actors/stakeholders and their roles and
responsibilities
Fieldsmen, Growers and their co-operatives
Goals To produce a modelling in order to predict the maturity of seed crops in order to organize harvest in the most efficient way and get mature, high quality seeds
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
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Current
solutions
Compute(System) On spot decisions made on the empiric experience of fieldsmen
Storage Local system + Company information system
Networking “Crop report” application on web, chat groups on Wats’app
Software Mobile application
Big data characteristics
Data source (distributed/centralized)
Availability of Sentinel-2 data (derived vegetation indices).
Scientific modelling – built phenology model.
Visualization – Processed data and model results are published in an intuitive way.
Volume (size) Hundreds of terabytes per year when all sources of data are considered.
Velocity
(e.g. real time)
Satellite data: Sentinel-2A+B images are acquired with a time step of 5 days. The images are pre-processed and distributed by ESA within 24 hours after acquisition. Further processing by VITO starts as soon as the images are available from ESA. Generally, the final information products become available for the end-users between 24 and 48 hours after image acquisition.
Telemetry IoT data: Time step for data collection is customizable, 1-60 minutes; big data: air temperature, air moisture, rainfall, soil temperature.
Phenotypic data are collected each cropping season.
Variety
(multiple datasets, mashup)
Great variety. (1) Satellite: imagery, multispectral data, indices (soil, water, vegetation, biophysical), (2) Telemetry IOT: air temperature, air moisture, rainfall, soil temperature. (3) analytics and phenotypic data.
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
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Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Need to have tools to produce and process ground-truth data for satellite data calibration.
Visualization Visualization of crop monitoring output at least bi-weekly during the cropping season, indices and predictions; real-time monitoring output, alerts, and recommendations.
Data quality (syntax) Data validity filtering w.r.t. completeness. Data fusion and modelling of heterogeneous data (EO data, telemetry IoT data, field data)
Data types Imagery, graphics, vector, numbers, analytical results, measurements, metadata, geolocations, spectra, time series.
Data analytics Predictive analytics for the development of data-driven yield models; predictive feedback (monitoring), real-time streaming data analytics to alert and provide operational recommendations using cloud-based crop management analytics including web portal cloud solution.
Big data specific challenges (Gaps)
There is a need for: (1) improving analytic and modelling systems that provide reliable and robust statistical estimated using large size of heterogeneous data; (2) reduced uncertainty of management decisions.
Big data specific challenges in bioeconomy
Delivering content and services to various computing platforms from Windows desktops to Android and iOS mobile devices
Security and privacy
technical considerations
Farm owner and geolocalization are highly sensitive, should be anonymized
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Real-time streaming data analytics and predictive analytics using machine learning for crop monitoring and developing yield models based on big data are universal solutions with domain agnostic applications.
More information (URLs)
www.databio.eu
<other URLs to be added later if relevant>
Note:
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4.2.1 Stakeholder and user stories
The end users are farmers, farming cooperatives, seed industry and research institutions.
VITO will use satellite data to monitor the crops and will develop maturity models. Telemetry
IoT technology will be implemented by C.A.C. to provide real-time analytics solutions.
Combining crop monitoring and real-time analytics will provide localized predictive plans to
improve farming operations and quality of harvest. Stakeholders and user stories are
summarized in the table below.
Table 9: Agriculture pilot A1.2 stakeholders and user stories
Stakeholders User story Motivation
Farmers, farming
cooperatives
want to monitor the maturity of
their seed crops throughout the
season
to evaluate the right time
for harvesting, optimize
field operations and get
high quality of their
products.
Fieldsmen Remote monitoring of maturity of
seed crops
Organise the harvesting
operations more
effectively, save time and
money.
4.2.2 Motivation and strategy
The main motivations for this pilot are:
• Identifying the potential for using satellite data and machine learning for monitoring crop development and maturity and the development of prediction models
• Evaluating the comparative importance between the use of proximal wireless sensor network data and satellite data
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
4.3 Pilot modelling with ArchiMate The current section presents the "Agriculture A.1.2 modelling with ArchiMate" view point
described using the ArchiMate standard.
4.3.1 Agriculture pilot A1.2 Motivation view
This section presents the "Agriculture A1.2 Motivation view" view defined in the "Agriculture
A.1.2 modelling with ArchiMate" view point.
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Figure 8: Agriculture pilot A1.2 Motivation view
4.3.2 Agriculture pilot A1.2 Strategy view
This section presents the "Agriculture A1.2 Strategy view" view defined in the "Agriculture
A.1.2 modelling with ArchiMate" view point.
Figure 9: Agriculture pilot A1.2 Strategy view
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4.4 Pilot Evaluation Plan
4.4.1 High level goals and KPI's
Three relevant KPIs have been identified so far:
• Model accuracy: maturity/yield prediction accuracy showing an acceptable error rate when tested on data that it was not trained on.
• Revenue potential with alternative cropping strategy vs. what happened: Quantify increased revenue potential on historical data, .e.g. what was the accumulated value, vs what could have been achieved using conventional cropping techniques.
• System usage: Number of users of DataBio technologies - yield models and proximal wireless sensors in seed crops. This is technology transfer and takes time to establish; for this project, a baseline will be measured first, then followed-up by monitoring usage after system deployment.
4.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 and 5 (WP4, WP5).
Figure 10: Agriculture pilot A1.2 initial roadmap
4.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.
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Figure 11: Agriculture pilot A1.2 BDVA reference model
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Pilot 3 [A1.3] Precision agriculture in
vegetables_2 (Potatoes) 5.1 Pilot overview
5.1.1 Pilot introduction
Potato has been the major crop in this region for many years. Due to the reform in CAP
(Europe’s Common Agricultural Policy) the market is changing and farmers are urged to
increase their yields, but in a sustainable way. This means they need to be more conscious
the energy and other resources they use in producing their crops. AVEBE is a cooperative for
the potato growing farmers and is supporting their growers in an innovation program called
Towards 20-15-10, a program which started in 2012. The objectives of this program are to
realize in 2020 an average of 15 tons of starch per ha with a variable cost price of €10 per 100
kg starch. To monitor these objectives farmers are sharing data about their yields and farming
practices in study groups.
5.1.2 Pilot overview
The goal of this pilot is to provide the potato farmers information during the growing season
about the potential and actual yield predictions and the actions they can take to mitigate the
foreseen yield losses. The pilot will supply the farmers with benchmark data about their crops
compared to the region, previous growing seasons etc. The data provided could also be the
basis for timely and more location specific treatment.
Method
The basis for the yield predictions will be the combination of data from different sources in a
self-learning system.
Using historical yield data and historical earth observation data, machine learning will be
applied to model the potato growth and calculate yield prediction for the current year, based
on recent earth observation data. The more yield (historical) yield data is supplied, the better
the predictions will be.
Specifically, as part of pilot solution, an online platform will be used to provide satellite
imagery, weather data and yield predictions. The farmer can use the satellite imagery
(biomass index, 10m resolution) to monitor and benchmark their field productivity potential
relative to production levels achieved in the region. After one year, yield prediction can be
implemented using the test data from year one, and other historical data (when available).
Relevance to and availability of Big Data and Big Data infrastructure
VITO has archives of earth observation (EO) data based on several satellite platforms. The
recently released Sentinel 2 (A and B) will be the most detailed sources, which we hope to be
able to use.
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The study groups have collected detailed field data for some years which will useful for
calibrating the system.
Benefit of pilot
This pilot will focus on the family farms in the Veenkoloniën region in the Netherlands, which
are members of the AVEBE cooperative. The main research partner will be VITO.
Especially these farms will benefit from this pilot, but as a spinoff the farms will be able to
grow their crops in a more sustainable way, which will be beneficial to all, farmers and the
people in the whole region.
5.2 Pilot case definition Current situation: Farmers monitor their crops just by their own observations and samples,
which is time consuming. Furthermore, the disadvantages are that is hard to create a good
overview based on just some observations and samples. Deviations in growth within the field
are hard to observe.
Pilot solution:
The solution that will be developed will consist of an online platform providing satellite
imagery with a 10m resolution (Sentinel 2A + B, possibly complemented with coarser
resolution data to overcome cloud problems), weather data, soil data and yield predictions.
The system will be based on the combination of data from different sources (a.o. Satellite
imagery) in a self-learning system, providing better predictions when more yield (historical)
yield data is supplied.
This first version will be the basis for the pilot; it will be “filled” with specific data, like varieties
of potatoes are grown, soils and weather conditions. To be able to provide yield forecasts
developments are needed. The coming year’s data we will collect data about the potato crops
that are grown at the pilot site in order to develop a model for yield prediction. In previous
years several study groups of farmers have collected data about their potato crops.
Potentially these historical yield data sources can also be used for ‘learning’ the model.
Each farmer who is participating in the pilot will provide crop data about his fields as input for
system learning. The farmer can use the satellite imagery (biomass index) to monitor his field
and benchmark his fields with other potato fields in the area. Benchmarking options will be
provided as well as multi-year comparison.
After one year yield prediction can be implemented using the test data from year 1, and other
historical data (when available).
Table 10: Summary of pilot A1.3 (ISO JTC1 WG9 use case template)
Use case title Precision agriculture in Potatoes; Benchmarking and yield prediction
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Vertical (area) Agriculture
Author/company/email Nicole Bartelds / NB Advies / [email protected]
Actors/stakeholders and their roles and
responsibilities
Farmer, responsible for growing potatoes in a sustainable way.
Cooperative / processing industry, responsible for processing potatoes while realizing the best price for the farmers
Goals Improve farming practices by providing benchmark information to the farmers.
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
Current
solutions
Compute(System) Non-existing system today
Storage Local system of study groups
Networking Not available online
Software Some standalone data management
Big data characteristics
Data source (distributed/centralized)
Combination of both types:
Centralized: EO data by VITO, weather data (Sentinel 2A + B, possibly complemented with coarser resolution data
Distributed: field characteristics (sample data yield data, potato varieties, planting data etc.) from different study groups
Volume (size) Terabytes per year: Primarily for EO data
Velocity
(e.g. real time)
Almost real-time (24h after acquisition) processed EO data from Sentinel 2 (potentially every 5 days), yearly crop/field data
Variety
(multiple datasets, mashup)
Medium variety of data sources EO: imagery, multispectral data, indices; weather: temperature, rain; crop/field characteristics
Variability (rate of change)
EO data potentially every 5 days
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
EO data in itself is not a reliable source for yield prediction. The system will need adequate calibration based on field data.
Visualization An online system should provide both a map view and tabular view of the
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benchmark data. There should be a time slider to follow data trends during the growing season.
Data quality (syntax) EO data should be cleaned from cloud and other atmospheric disturbances
Data types Image data / services for EO data, WFS webservices from LPIS for field boundaries, API for weather data, local data sources for crop/field characteristics and yield data
Data analytics Predictive analytics using machine learning; real-time streaming data analytics to alert and provide operational recommendations
Big data specific challenges (Gaps)
Big Data needs to deliver added-value integrated services. The farmers will need actionable information. Creating an early warning service for yield or quality deviation a farmer can act upon will be challenging.
Big data specific challenges in bio-
economy
The Earth observation data will be challenging in sense of dealing with the Volume of data. Whereas the farm data will be challenging in Variety of data coming from different sources, in different formats, using different semantics.
Security and privacy
technical considerations
Privacy
● Farmer is in control about the level of data sharing ● Published farm data should be aggregated to avoid that
data is traceable to one specific farm ● .. more privacy requirements from the LTO privacy
baseline on sharing farm data Security
● Access for registered users only ● Registration reserved for involved stakeholders
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Predictive analytics using machine learning based on big data is a general problem that extends to all the three sectors in DataBio and beyond.
More information (URLs)
www.databio.eu
https://watchitgrow.be/en
Note: <additional comments>
5.2.1 Stakeholder and user stories
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Table 11: Agriculture pilot A1.3 Stakeholders and user stories
Who (type of user) I want to (can you perform
some task)
Why (achieve some goals)
farmers want to monitor the growth
their potato crop during the
season
to evaluate possible actions
like fertilizing and crop health
measures
farmers want to evaluate differences
in plant growth between and
within fields
to evaluate possible actions
like fertilizing and crop health
measures for specific areas
farmers want to evaluate weather
data, planting date and yield
for one or several years
to explain yield differences
and evaluate possible tactical
actions for future seasons
cooperative want a platform for growers to support their growers in
achieving better yields
cooperative want better yield predictions to improve their processing
capacity, planning and sales
cooperative want sufficiently good yield
predictions based on satellite
imagery
to reduce costs for field
sampling
5.2.2 Motivation and strategy
The main motivations for this pilot is:
• to identify the potential of using of satellite data and machine learning to benchmark and optimize the yield and quality of the potato crops through the development of a monitoring and yield prediction model based on weather and EO data
• to identify the potential of yield prediction to improve capacity planning and sales forecasts.
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
5.3 Pilot modelling with ArchiMate The current section presents the "Agriculture A.1.3 modelling with ArchiMate" view point
described using the ArchiMate standard.
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5.3.1 Agriculture pilot A1.3 Motivation view
This section presents the "Agriculture A1.3 Motivation view" view defined in the "Agriculture
A.1.3 modelling with ArchiMate" view point.
Figure 12: Agriculture pilot A1.3 Motivation view
Yield prediction of crops is committed to agronomists who go to reference fields to collect
samples. The activity implies recording of information on the status of crops to the
cooperatives office. Furthermore, agronomists assist the growers in deciding the timing of
each field operation, such as sowing/transplanting time, cultivation, spraying, irrigation,
harvesting.
The fields are scattered on a wide area and agronomists consume time to visit each field which
are in distances of several kilometres. There are therefore limitations to the number of
growers which each agronomist can co-ordinate.
5.3.2 Agriculture pilot A1.3 Strategy view
This section presents the "Agriculture A1.3 Strategy view" view defined in the "Agriculture
A.1.3 modelling with ArchiMate" view point.
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Figure 13: Agriculture pilot A1.3 Strategy view
Cost-effective, high-resolution solutions will be implemented in this pilot to substitute for the
conventional farm monitoring methods. The new technologies are expected to improve
insight about the impact of farming practices in order to sustainably improve productivity and
profits.
5.4 Pilot Evaluation Plan
5.4.1 High level goals and KPI's
Three relevant KPIs that has been identified so far:
• Prediction quality: Evaluate the correctness of the model by testing on historic data that the system was not trained on.
• Revenue potential with potential yield vs. what happened: Quantify the value of the potential yield and the actual realization on historical data, and motivate the price of the service which would be profitable for the farmer.
• Improved ratio of realized yield to potential: Visualize the trend of yield optimisation over time.
5.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 and 5 (WP4, WP5).
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Figure 14: Agriculture pilot A1.3 initial roadmap
5.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.
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Figure 15:Agriculture pilot A1.3 BDVA reference model
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Pilot 4 [A2.1] Big data management in
greenhouse eco-system 6.1 Pilot overview
6.1.1 Pilot introduction
The overall objective of the proposed pilot is to implement Genomic Selection (GS) Models in
support for greenhouse horticulture value chain, with particular focus on tomato. Genomics,
metabolomics and phenomics data will be integrated. Genomic selection is a new paradigm
in population genetic improvement that uses a larger number of genome-wide distributed
molecular markers to predict individual breeding values. The GS has demonstrated superior
performance in comparison with the methods used in breeding for quantitatively inherited
characters, i.e., phenotypic selection and quantitative trait loci approaches (Bernardo and Yu,
2007; Heffner et al., 2011; Lorenzana and Bernardo, 2009). The superiority of the GS strategy
is mostly associated with higher accuracy in predicting the individual’s genetic merit and the
shortening of a breeding cycle due to intercrosses driven by genetic predictions, which results
in higher genetic gain per unit of cost and time. These GS attributes are expected to have
wide-range implications in this pilot as the cost of cultivar development is going to be
reduced. Therefore, farmers can grow a better tomato variety sooner due to rapid variety
development and release, making more income. GS in greenhouse tomato is expected to
exert significant impact on the market potential and industry interests in this crop. Indeed,
tomato is among the top cultivated crops in greenhouses, with billions of euros turnover
worldwide. Tomato is considered one of the most nutritive solanum vegetables due to its high
content in sugars, vitamins and antioxidants and its consumption is steadily increasing.
6.1.2 Pilot overview
The pilot will be run by a close partnership between CREA and CERTH, and will build upon
ongoing greenhouse horticulture breeding works in the Thessali Region, Greece, where
tomato materials are grown throughout the year in two greenhouses (2ha) and 2 walking
growth chambers. CREA and CERTH will share complete complementary tasks with the former
handling genomic predictions and selection, while the latter will be responsible for
phenomics, metabolomics, genomics and environmental datasets acquisition. The end users
of this pilot include farmers and farming cooperatives who currently grow crops following
standard farming practices and selection based on phenotypes, which is time and resource
consuming, with low resolution and efficiency. The end users therefore want cost-effective,
high-resolution solutions capable of expediting breeding activities in order to simplify
breeding scheme, shorten the time to cultivar development; selecting for genetic merit
estimated through genomic modelling in order to sustainably improve productivity and
profits. Within the DataBio framework, the services that are expected to be provided include
mainly farmer-customized estimates and selection for individual (plant, genotype) genetic
merit for a trait of interest, or several traits of interest for the farmer aggregated in Index.
Method
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The pilot focuses mainly on implementing models based on a combination of genomic and
observational/environmental data to be applied in horticultural crop breeding in greenhouses
ecosystems in the Thessalia region, Greece. CERTH developed and applied high throughput
phenotyping and whole-genome genotyping systems to sustain modern high throughput
horticultural cultivar development. Current and historic information of breeding interest will
be gathered and used. On the other hand, CREA developed and applied high-resolution and
high throughput breeding approach based mainly on genomic models that demonstrated
superior predicting ability in several crop breeding systems including cereals and horticultural
crops. The two technologies at CREA and CERTH will be tandemly used in this pilot.
Specifically, parametric and nonparametric models will be implemented to anticipate the
possibility for their alternative use depending particularly upon the type of information
available. The potential of these models will be evaluated based on the predictive ability for
individual genetic merit. The implemented algorithms will differ in the assumptions of the
distribution of marker effects, and can therefore offer the possibility to account for different
models of genetic variation. Some models are expected to be suited to infinitesimal model
assumptions, others are expected to be best suited to finite loci model, whereas other models
extend Fisher’s infinitesimal model of genetic variation to accommodate non-additive genetic
effects. The different priors within the genomic models combined with the likelihood function
lead to joint posterior densities from which is possible to draw values from the fully
conditional densities using appropriate sampler. Phenotypic and whole-genome molecular
data will be fed into the models after the quality was checked and filters applied as
appropriate. One key breeding problem modelled includes predicting the performance of new
and unphenotyped lines/genotypes.
Cost-effective, high-resolution solutions will be implemented in this pilot to substitute for the
conventional breeding methods. The new technologies are expected to expedite breeding
activities, simplify breeding scheme, and shorten the time to cultivar development; selecting
for genetic and phenotypic merit estimated through genomic modelling in order to
sustainably improve productivity and profits. Specifically, metabolomics data (LC/MS/MS,
GS/MS, HPLC) will be collected. Other phenotypic data will include: (1) environmental indoor
air temperature, air relative humidity, solar radiation, crop leaf temperature (remotely and in
contact), soil/substrate water content; (2) environmental outdoor wind speed and direction,
evaporation, rain, UVA, UVB; (3) farm in-situ measurements (soil nutritional status testing),
farm logs (work calendar, technical practices at farm level, irrigation information), and farm
profile (static farm information, such as size, crop type, etc.). Genomic data will be derived
through Next Generation Sequencing protocols to generate whole or partial genome
sequences, transcriptome, and genotypic datasets. High quality phenotypic and genomic
information will be integrated in the process of genomic modelling. Models will be tested,
and those with higher predicting ability identified and implemented on a breeding scenario-
by-scenario basis. Contrary to conventional phenotype-based breeding, varietal selection in
this pilot will be executed based only on molecular marker information.
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Relevance to and availability of Big Data and Big Data infrastructure
This pilot will build upon ongoing greenhouse horticulture breeding works in Thessalia
(Greece) and on peer-reviewed genomic prediction algorithms already in use at CREA. On the
other hand, large amounts of data were gathered at CERTH including metabolomics data
collected using LC/MS/MS, GS/MS, HPLC tools, whole genome, transcriptomes, and genotypic
datasets generated using Next Generation Sequencing protocols. Environmental indoor
collected data including Air temperature, Air relative humidity, Solar radiation, Crop leaf
temperature (remotely and in contact), Soil/substrate water content, while outdoor data
include wind speed and direction, Evaporation, Rain, UVA, UVB. Farm Data include in-situ
measurements (soil nutritional status testing), farm logs (work calendar, technical practices
at farm level, irrigation information,), and farm profile (Static farm information, such as size,
crop type, etc.). CERTH will provide NGS Sequencing machines (Illumina MiSeq and NextSeq),
HPLC, GC-MS, LC-MS, and access to the HPC system to perform primary analysis of NGS and
Metabolic data.
Benefit of pilot
The genomic data and relevant analytics will be directly useful to the involved Greek farmers
and farming cooperatives located in the Region of Thessali. The pilot is expected to directly
improve horticulture productivity and profits due not only to increased yields, but also to the
reduction of farming costs and risks, and the early variety release. Higher yields are expected
in virtue of high predicting ability of the genomic algorithms deployed to select for superior
genotypes. The breeding costs will be drastically cut in virtue of the adoption of simplified
breeding schemes, reduction of phenotypic evaluations, and shortening the breeding cycle.
Early variety release is an intrinsic GS property, particularly in virtue of genetic merit-driven
intercrosses. Therefore, farmers can grow a better variety sooner due to rapid variety
development and release, making more income. In addition, the results of this pilot will help
undertake further extension services endeavours and investigations to improve the whole
genome data collection and the predicting ability of the GS algorithms. The implantation of
accurate models under appropriate breeding scenarios, and the use of high quality NGS
sequencing machines and HPC system was anticipated to ensure that the proposed solutions
can be dependable and replicable to other crop types and market segments. Scientific papers
along with large amounts of data will be produced, preserved and made FAIR (Findable,
Accessible, Interoperable, Reusable) to further science and knowledge for the wellbeing of
man.
6.2 Pilot case definition
Table 12: Summary of pilot A2.1 (ISO JTC1 WG9 use case template)
Use case title Genomic Selection in greenhouse horticulture
Vertical (area) Agriculture
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Author/company/email 1. Ephrem Habyarimana / CREA / [email protected]
2. Anagnostis Argiriou / CERTH / [email protected]
Actors/stakeholders and their roles and
responsibilities
CERTH/CREA/Farmers, farming cooperatives, responsible for technological, farming and operational decisions.
Agroindustry/consumer, responsible for the manufacturing quality, sustainability, the business push, and market pull.
Goals Using environmental and whole genome biochemical data, and genomic prediction algorithms to predict horticultural species genetic merit upon which superior ideotype are identified and selected. Refer to the evaluation section for specific goals and KPIs.
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
Current
solutions
Compute(System) HPC Cluster, Workstations, PCs, Laptops (Xeon, CORE i7, etc.), standards OSs. Phenotypic data are processed and analysed. Sensor data and whole-genome biochemical information are not harnessed yet.
Storage RAID storage (approx. 100 TB) in the premises of CERTH/INAB
Networking Social networks: LinkedIn, Facebook, Twitter
Software Multiple individual algorithm systems, not integrated processing and display.
Big data characteristics
Data source 1. Biochemical: LC/MS/MS, GS/MS, HPLC to collect metabolomics data;
2. Genomic: Next Generation Sequencing protocols to generate genomic, transcriptomic, genotypic dataset
3. Environmental indoor: Air temperature, Air relative humidity, Solar radiation, Crop leaf temperature (remotely and in contact), Soil/substrate water content
4. Environmental outdoor: Wind speed and direction, Evaporation, Rain, UVA, UVB
5. Farm Data: In-Situ measurements: Soil nutritional status testing;
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Farm logs (work calendar, technical practices at farm level, irrigation information,); Farm profile (Static farm information, such as size, crop type, etc.).
Volume (size) 10 to 100 Gb depending on the compression level.
Velocity
(e.g. real time)
Variety
(multiple datasets, mashup)
Great variety: genomic, phenomic, metabolomics, environmental public data and analytics data.
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Visualization Visualization of crop monitoring output and GS predictions.
Data quality (syntax) Data validity filtering w.r.t. completeness and dependability.
Data types Experimental: whole genome genotypic data, metabolomic and phenomic (lab) data. Mainly ASCII files (fastq and text files).
Observational: phenomics (field), sensor data, environmental (Environmental indoor: air temperature, air relative humidity, solar radiation, soil/substrate water content).
Data analytics Predictive analytics genetic merit and generation of selection indices, transcriptomics and metabolomics analytics, genomic assembly and annotation algorithms.
Big data specific challenges (Gaps)
There is a need for: (1) cost-effective, high-resolution solutions capable of expediting breeding activities in order to simplify breeding scheme, shorten the time to cultivar development; selecting for genetic merit estimated through genomic modelling in order to sustainably improve productivity and profits; (2) farmer-customized GS for a trait of interest, or several traits of interest for the farmer aggregated in Index; (3) closing the gap
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between agricultural business planning and the responsible and sustainable maximization of the profit deriving mainly from increased crop productivity and efficiency of resource use, reduced uncertainty of management decisions, accounting for environmental standards and regulations.
Big data specific challenges in bioeconomy
Delivering content and services to various computing platforms from
Windows desktops to Android and iOS mobile devices
Security and privacy
technical considerations
Greenhouse owner and geolocalization are highly sensitive, should be anonymized
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Genomic prediction analytics based on environmental data, phenomics, and whole-genome biochemical information are universal solutions with domain agnostic applications.
More information (URLs)
www.databio.eu
<other URLs to be added later if relevant>
Note:
6.2.1 Stakeholder and user stories
The end users are farmers, farming cooperatives, agroindustry and research and
technological institutions. CERTH implemented methodologies and know how to produce and
process useful: (1) experimental data: whole genome genotypic data, metabolomic and
phenomic (lab) data; (2) observational data: phenomics (field), sensor data, environmental
indoor and outdoor. On the other hand, CREA developed GS algorithms that demonstrated
high predicting ability in different crops including Solanaceae species, the family which
tomato belongs to.
Table 13: Agriculture pilot A2.1 stakeholders and user stories
Stakeholders User story Motivation
Farmers, farming
cooperatives
want cost-effective, high-
resolution breeding solutions.
To select the best possible and
affordable varieties without
confounding effects of the
environment, which guarantees
consistent and dependable
yields.
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Farmers, farming
cooperatives
want a simplified and less time-
consuming breeding scheme
to efficiently shorten the time to
bring the new varieties to
market and maximize the profits
over the longer variety’s life
cycle.
Farmers, farming
cooperatives
want to efficiently and accurately
breed for varieties with high
nutritional (health-promoting
natural biochemicals) and trading
values
to responsibly target, promote
and/or extend market segments
that are ready to pay for
additional quality of the
produce.
6.2.2 Motivation and strategy
The main motivations for this pilot are
• to predict the performance of unphenotyped tomato materials in grasshouse agroecosystems using molecular data information.
• to empirically demonstrate the benefit of using genomic data in terms of increasing genetic gain by unit cost and time
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
6.3 Pilot modelling with ArchiMate The current section presents the "Agriculture A.2.1 modelling with ArchiMate" view point
described using the ArchiMate standard. It lists the views and nomenclatures composing the
view point.
6.3.1 Agriculture pilot A2.1 Motivation view
This section provides the "Agriculture A2.1 Motivation view" view defined in the "Agriculture
A.2.1 modelling with ArchiMate" view point.
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Figure 16: Agriculture pilot A2.1 Motivation view
Crops are grown following standard farming practices and selection based on phenotypes,
which is time and resource consuming, with low resolution and efficiency. On the other hand,
frequent and regular biochemical analyses to sustain conventional breeding add greatly to
the cost of breeding activities with consequent high costs for the developed cultivars.
6.3.2 Agriculture pilot A2.1 Strategy view
This section provides the "Agriculture A2.1 Strategy view" view defined in the "Agriculture
A.2.1 modelling with ArchiMate" view point.
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Figure 17: Agriculture pilot A2.1 Strategy view
Cost-effective, high-resolution solutions will be implemented in this pilot to substitute for the
conventional breeding methods. The new technologies are expected to expedite breeding
activities, simplify breeding scheme, and shorten the time to cultivar development; selecting
for genetic and phenotypic merit estimated through genomic modelling in order to
sustainably improve productivity and profits.
6.4 Pilot Evaluation Plan
6.4.1 High level goals and KPI's
Three relevant KPIs have been identified so far:
• Model accuracy: performance prediction accuracy showing an acceptable error rate including when the testing and training sets are genetically distant.
• Revenue potential with alternative cropping strategy vs. what happened: Quantify increased revenue potential GS versus phenotypic selection.
• GS take-up: number of vegetable growers adopting GS approach. This is technology transfer and takes time to establish; for this project, a baseline will be measured first, then followed-up by monitoring usage after system deployment.
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6.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 (WP4).
Figure 18: Agriculture pilot A2.1 initial roadmap
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6.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component name.
Figure 19: Agriculture pilot A2.1 BDVA reference model
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Pilot 5 [B1.1] Cereal, biomass and cotton
crops_1 7.1 Pilot overview
7.1.1 Pilot introduction
The pilot aims to develop an accurate "irrigation maps" and "vigor maps" (combining EO data
and sensors data) which allows mapping different areas in Spain - beginning in Castilla -and
set up an informative and management system for early warning of inhomogeneity.
Those new services will be dedicated to the analytical and accurate finding of heterogeneities
in crops related to irregular irrigation, mechanical problems affecting irrigation systems,
incorrect distribution of fertilizers or any other sources of inhomogeneity that could explain
crops growing differences. This Service will be a powerful preventive tool for general farmers
and land owners in order to avoid production losses.
7.1.2 Pilot overview
This service aims to provide information for precision agriculture, mainly based on time series
of high resolution (Sentinel-2 type) satellite images, complemented with sensor data and, in
some specific cases, with RPAS data. The information can be used as input for farm
management (operational decisions, tactical decisions). Information layers may include:
Vegetation indexes (NDVI, Normalized green red difference index) and derived anomaly
maps.
This service will offer cost saving for farmers communities due to a better quality
management in agricultural zones, especially focused on irrigated crops. Monitoring and
managing irrigation policies and agricultural practices will offer meaningful water and energy
saving. Besides this, fertilizers control and monitoring can produce, eventually, a prominent
economic saving per year and hectare. This better management of hydric and energetic
resources is also related to Green-house effect gases reduction, directly linked to better
environmental conditions in agriculture.
Table 14: Summary of pilot B1.1 (ISO JTC1 WG9 use case template)
Use case title B1.1 TRAGSA
Vertical (area) Cabreros del Río, Castile, Spain
Author/company/email
Sofía Iglesias/TRAGSA Group/[email protected]
Jesús Estrada/TRAGSA Group/[email protected]
Actors/stakeholders and their
TRAGSA
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roles and responsibilities
Goals Develop an accurate “Irrigation maps” and “Vigor maps”
Use case description
Combining EO data and sensor data which allow mapping different areas in Spain and setting up an informative and management system for irrigation and early warning of heterogeneity or malfunction of irrigation systems and devices.
Current
solutions
Compute(System)
Dedicated server
2 processors Intel(R) Xeon(R) CPU E5606 @ 2.13GHz, 8
cores with 96 GB ECC of Memory
Storage Dedicated server
Hard Disks: 2 disks - 2 TB (RAID). Total: 2 TB
Networking 250 MBps
Software OS: Debian 8.8
Apache web server 2.4.10
Tomcat: 7.0.56 and 8.0.14
R: 3.3.3.
PostGres: 9.4
MySQL: 5.5.55
Python: 2.7.9
Virtuoso: 07.20.3212
Big data characteristics
Data source (distributed/cen
tralized)
Combination of both types:
Centralized: Remote sensing data as Sentinel 2B data provided by ESA at Sentinel Data Hub (https://cophub.copernicus.eu/)
Orthophotos (Spanish Coverage). RGB and NIR bands provided by Spanish National Geographic Institute at http://centrodedescargas.cnig.es/CentroDescargas/catalogo.do
Surveys and field data (Confidential)
Likely, GEOSS open sources available at GEOSS portal (http://www.geoportal.org) will be used as testing and validation data
Distributed/local:
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LPIS system is provided in a distributed way by NUTS2-Level Administration. Castile LPIS system is accessible at
http://ftp.itacyl.es/cartografia/05_SIGPAC/2017_ETRS8
9/Parcelario_SIGPAC_CyL_Provincias/
TRAGSA Drones produce JPEG and LAS files using thermal and multispectral sensors. More information is available in the DataBio deliverable D6.2 – Data Management Plan
Volume (size) Remote sensing data as Sentinel 2B have an average size of TB per year. LPIS Spanish system has a size of hundreds of Gigabytes, likewise the Spanish Orthophoto project (PNOA).
RPAS data has a size of Tb per year.
Field data information has a size of Mb per year.
Velocity
(e.g. real time)
Sentinel 2B has the highest updating rate within Pilot Information sources (5 days). All external sources have a yearly updating ratio.
Variety
(multiple datasets, mashup)
The formats to be used will be imagery and terrain models.
Variability (rate of change)
Agricultural information, typically, depends on seasons. Highest variability rate is few days (2-3)
Big data science
(collection, curation,
analysis,
action)
Veracity (Robustness
Issues, semantics)
All data sources are official and trusted ones: European Space Agency (ESA) and Spanish Public Administration.
Visualization Standard imagery visualization services. Spanish Public Administration usually provides WMS services for information visualization.
Data quality (syntax)
Despite of the data providers are supposed to produce good quality information, all datasets are processed by TRAGSA to produce improved images. Specifically, orthophotos will be transformed by an orthorectification method developed under WP5.
Data types JPEG or JPG2000 for images, .LAS for terrain models. Text document for surveys and field data.
Data analytics Anomaly maps will be used to answer questions about
distribution of plant protection products or correct and
consistent growing of the crops.
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Big data specific
challenges (Gaps)
TRAGSA Group is currently able to process, manage and submit results about a high number of parcels. Processes will be scaled to provincial, regional and national levels.
Big data specific
challenges in bio-economy
TRAGSA Group is currently able to process, manage and submit results about a high number of parcels. Processes will be scaled to provincial, regional and national levels. In regard to Farmers Communities, the aim is to provide similar services to more than ten new different Communities.
Security and privacy
technical considerations
No datasets of this pilot are considered sensitive or related to personal data.
Highlight issues for generalizing
this Use case (e.g. for ref.
architecture)
Main technical problems are related to data storage and resulting maps transmission.
More information
(URLs)
ESA: https://sentinel.esa.int/web/sentinel/sentinel-data-access
PNOA: http://www.ign.es/web/ign/portal/obs-portal-pnoa
GEOSS: https://www.earthobservations.org/index.php
Note: No additional comments.
7.2 Pilot case definition Location: Cabreros del Río, Leon, Spain. Potentially more Farmers Communities after the
second year of the project.
Supported by: “Ribera del Porma” Farmers Community
Area size: “Ribera del Porma” Farmers Community: 24.270 ha
7.2.1 Stakeholder and user stories
Table 15: Agriculture pilot B1.1 stakeholders and user stories
Who (type of user) I want to (can you
perform some task)
Why (achieve some goals)
Farmers, irrigations communities
(end users)
To know current
status of crops
To visualized updated (and
preventive maps) to avoid future
risks.
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vigor in specific
parcels.
Farmers, farming cooperatives
(end users)
To know accurate
temporal evolution
of water demand.
To visualized updated (and
preventive maps) to concentrate
efforts and save field visits.
Cost Savings.
Farmers, farming cooperatives
(end users)
To better define the
places for RPAS
visits, in the case
they were
necessary.
To concentrate efforts and save
costs and resources.
GIS Technician and IT teams
(TRAGSA, as manager of Irrigation
Community, or SMEs and other
third parties)
To produce
updated irrigation
and vigor maps.
To provide automated, high-quality,
highly-updated and cheap products
for:
- manage the water supply
- check the available
information of the parcels
- manage the crops
7.2.2 Motivation and strategy
Therefore, in this pilot will be developed the following:
• Agricultural Heterogeneity Analysis for analytical and accurate finding of heterogeneities in crops related to irregular irrigation, mechanical problems affecting irrigation systems, incorrect distribution of fertilizers or any other sources of inhomogeneity that could explain crops growing differences.
• Irrigation services improved using the information provided by the previous point
These services will be a powerful preventive tool for general farmers and landowners in order
to avoid production losses.
7.3 Pilot modelling with ArchiMate
7.3.1 Agriculture pilot B1.1 motivation view
This service will offer Public Administrations, Farmers Communities, Rural owners and
Farmers a remote plant disease diagnosis and assessment based on the processing of Satellite
images. Besides this, this service will monitor irrigation systems performance in order to show
accurate crop strength maps. Efficient soil fertilization procedures based on precise measures
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will be a direct consequence of Satellite Data use. These products will be provided through
web services.
Below the "Agro Pilot B1.1 Motivation view" view defined in the "Agro Pilot 1.3.1 View Points"
view point is presented.
Figure 20: Agriculture Pilot B1.1 TRAGSA Motivation view
7.3.2 Agriculture pilot B1.1 strategy view
Irrigated agriculture is responsible for compensating the agricultural trade balance in
countries with agro-climatic conditions that are unfavourable to agriculture. Water shortages
make it necessary to have powerful systems that help provide the plant with the minimum
water needed to ensure high productivity crops. Therefore, the use of advanced technologies
(remote sensing) is very important in order to maximize the benefits of irrigated agriculture
at the lowest cost. On the other hand, the use of these technologies also provides accurate
information to anticipate health problems in crops and to prevent them in advance, which
allows preserving most of the crop in optimal sanitary conditions.
Below the "Agro Pilot B1.1 TRAGSA Strategy view" view defined in the "Agro Pilot 1.3.1 View
Points" view point is presented.
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Figure 21: Agriculture pilot B1.1 Strategy view
7.4 Pilot Evaluation Plan
7.4.1 High level goals and KPI's
Three relevant KPIs have been identified so far:
• Surface processed, from the original 24.270Ha.
• Field visits saved and their economic impact.
• Economic improvements: water, energy and other resources savings due to better information on crops status.
In the current development state of the pilot is not possible to define accurate quantitative
figures for the previous KPIs. In the following and intermediate reports, these KPIs will be
improved from the current explanatory state to a more rigorous definition.
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7.4.2 Initial roadmap
Figure 22: Agriculture pilot B1.1 initial roadmap
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7.5 Big data assets
Figure 23: Agriculture pilot B1.1 BDVA reference model
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Pilot 6 [B1.2] Cereal, biomass and cotton
crops_2 8.1 Pilot overview
8.1.1 Pilot introduction
Farming 4.0 [REF-08] holds the key for meeting the rising demands for increased yield at all
farm levels. The agricultural sector faces unprecedented challenges to cope with recent
trends (precision farming, automation, consolidation, professionalism, labour shortage, etc.)
that shape a continuously evolving farming environment. It is evident, that “producing more
with less” is more than a will, but rather a “must-have” requirement for future farmers, with
the decision making process playing a vital role in farm profitability. Based on a review of 234
studies that were published from 1988 to 2005, precision agriculture was determined to be
the reason for a profit increase in 68% of the cases. From an economic point of view, farm
profitability comes as a result of following different management strategies. In a market that
sometimes is struggling to remain afloat, farmers are aiming to use technological
advancements for cost reduction primarily, without any significant impact on their
production. Thereby, a major expected benefit from precision agriculture derives from the
optimization of inputs (e.g. fresh water consumption) and of farm management (leading to
cost reduction) with farm size being a critical parameter for farm profitability.
8.1.2 Pilot overview
The main focus of this pilot is to offer smart farming services dedicated for arable crops and more specifically cotton, based on a set of complementary monitoring technologies. Smart farming services are offered as irrigation advices through flexible mechanisms and UIs. The pilot will target towards promoting the adoption of technological tools (IoT, Big Data analytics, EO data) and collaborating with certified professionals to boost/optimize farm profitability. NP and GAIA will support the activities for the execution of the full life-cycle of the pilot.
Table 16: Agriculture pilot B1.2 overview of pilot activities
Pilot Site
Location Kileler, Greece
Area Size 5000ha
Targeted Crops Cotton
End-Users Group of farmers, Agronomists
Current Situation Cultivation of cotton using standard farming practices
Method
This pilot is targeting towards providing a smart farming services to the farmer utilizing
available precision agriculture techniques. The services will be provided as irrigation advices,
which need many prerequisites and primary material in order to be accurate. Data is the raw
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material and there are three (3) different means of collecting data, which will be exploited
within the pilot activities. Data directly from the field, collected from a network of telemetric
IoT stations called GAIAtrons; remotely with image sensors on in-orbit platforms; and by
monitoring the application of inputs and outputs in the farm (e.g. in-situ measurements, farm
logs, farm profile). Every data source has unique characteristics with relevant impact on the
very content of this data. Field sensing provides real-time accurate direct measures of many
physical parameters of the soil, atmosphere microclimate of the field crop and plant with
temporal continuity. Remote sensing provides indirect measures of some physical properties
of plants and soil (we will explore measuring evapotranspiration, which is an important part
of the water cycle) with spatial continuity in medium to large spatial scale. Combining this
information can provide a good knowledge of the most important physical parameters of soil,
microclimate, plants and water (which are all the environmental resources, which govern
farming) in both spatial and temporal dimensions. Monitoring the application of inputs and
outputs on the farm is a data element that is necessary to assess the correctness of the given
advice and use it as feedback to improve the system over time. This pilot will combine
advanced data handling techniques (i.e. assimilation, fusion and spatio-temporal
interpolation) to transform the collected data into actionable irrigation advice. In order for
this advice to better reflect the actual situation at a given field, we will seek to incorporate
the human experience of the farmer or certified advisors.
Relevance to and availability of Big Data and Big Data infrastructure
NP has already started collecting field-sensing data through its network of telemetric IoT
stations, called GAIAtrons. GAIAtrons offer configurable data collection and transmission
rates. Specifically, for the region of Kileler, 4 GAIAtron stations (2 GAIAtron Atmo and 2 Soil)
collect and transmit several atmospheric and soil-related measurements in NP’s cloud
infrastructure contributing in several dimensions of big data (velocity, variety, value). Within
the same cloud infrastructure (GAIA cloud), high volume remote sensing data from the new
Sentinel 2 optical products are being extracted and stored since the beginning of 2016. This
comprises both raw and processed (corrected products, extracted indices) data represented
in raster formats that are being handled and distributed using optimal big data management
methodologies. Finally, a valuable data source comes from electronic farm logs that NP offers
to the farmers and can be exploited for fine-tuning and improving the quality of the provided
services within the specific pilot’s (crop and region specific) context.
Benefit of pilot
The pilot is expected to have a direct impact on farm profitability in a crop type (cotton) with
significant value, from an economic perspective. The holistic approach that is being proposed
will significantly improve the capacity of the responsible partners (NP, GAIA) in providing
smart farming advisory services. In addition, it would lead to major improvements in a) GAIA
cloud’s stability, availability, security, interoperability and overall maturity, b) GAIABus
DataSmart/GAIA SmartFarm functionality in terms of real-time analytics, data stream and
decision support processes, multi-temporal object-based monitoring and cloud-based
services.
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8.2 Pilot case definition
Table 17: Summary of pilot B1.2 (ISO JTC1 WG9 use case template)
Use case title Cereals, biomass and cotton crops_2
Vertical (area) Agriculture
Author/company/email NP, GAIA Epicheirein
Actors/stakeholders and their roles and
responsibilities
• Group of farmers, responsible for performing farming activities
• Agronomists, involved in providing relevant and up-to-date advices to the farmers
Goals Provide smart farming advisory services (focusing on irrigation), based on a set of complementary monitoring technologies, in order to increase farm profitability and promote sustainable farming practises.
Use case description Refer to the pilot case definition section and diagrams in the pilot modeling sections.
Current
solutions
Compute(System) Non-existing system today, cultivation is being performed using standard (long-established) farming practices
Storage Electronic farm logs are rarely used by farmers.
Networking -
Software -
Big data characteristics
Data source (distributed/centralized)
Centralized: Field sensing data from GAIAtrons, Remote sensing (Earth observation) data, Farm data
Volume (size) • several TBs/year for remote sensing data, including raw data and extracted indices for the pilot areas
• several GBs/year field sensing data collected by the deployed GAIAtrons (2 GAIAtro Atmo and 2 GAIAtron soil)
Velocity
(e.g. real time)
Configurable data transmission rates for field sensing, per monitored parameter, based on the needs of the application (provide actionable irrigation advices).
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Every 10 days for EO products before 2018. Currently EO products are available every 5 days.
Variety
(multiple datasets, mashup)
Field Sensing: Soil temperature, humidity (multi-depth), ambient temperature, humidity, barometric pressure, solar radiation, leaf wetness, rainfall volume, wind speed and direction.
Remote Sensing: 13 spectral bands
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Need for a system that can constantly provide relevant and up-to-date advices to its end-users.
Visualization Spatio-temporal information visualization for improving farm management and facilitating the decision-making process.
Data quality (syntax) The quality of field sensing data is being controlled by several filtering, outlier detection and stream processing mechanisms. The integrity of remote sensing data quality is being assessed by a hash check upon product download.
Data types Remote sensing data provided in raster format (.jp2). Field sensing data provided as time series unstructured data with configurable frequency.
Data analytics Descriptive and prescriptive analytics for the provision of irrigation advices.
Big data specific challenges (Gaps)
There is a need for smarter fusion of the heterogenous data types that are being collected towards providing accurate insights. To this end, it is important to explore mechanisms that could combine raster and vector data at parcel level (polygon) and station level (point). Moreover, in order to facilitate the adoption of the big data technologies by the farmers, imposed barriers in data visualization should be encountered (e.g. give more emphasis to vector data, improvement of the aggregation mechanism (drill down, zoom in, roll up, zoom out,)).
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Big data specific challenges in bioeconomy
A system intended to collect data from field sensors, installed in remote locations, is definitely going to face network connectivity challenges. In order to provide up-to-date and relevant advices, the system should be able to exhibit high availability and accuracy in its sensor readings and transmission mechanisms.
Security and privacy technical considerations
Field sensing data should be securely transmitted to the cloud infrastructure and protected against various types of attacks that might set the system at risk.
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
EO data management mechanisms can be exploited for other use cases where EO data might provide valuable insights
8.2.1 Stakeholder and user stories
Table 18: Agriculture pilot B1.2 stakeholders and user stories
Stakeholders User story Motivation
Farmer As a farmer I want to follow an irrigation plan based on my crop needs and farm characteristics.
Increase my profits following sustainable agriculture practices
Agronomists As an agronomist I want to have a comparative advantage in a highly competitive market and to offer the best possible services to my clients
Increase my profits by providing better advices based on evidences, well-established arguments and scientific knowledge.
8.2.2 Motivation and strategy
The main motivation for this pilot is:
• to raise the awareness of the farmers, agronomists, agricultural advisors, farmer cooperatives and organizations (e.g. group of producers) on how new technological tools could optimize farm profitability and offer a significant advantage on a highly competitive sector.
• to promote sustainable farming practises over a better control and management of the resources (water).
• to increase the technological capacity of the involved partners through a set of pilot activities that involves management of big data for high value crops.
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8.3 Pilot modelling with ArchiMate
8.3.1 Agriculture pilot B1.2 Motivation view
This section describes the "Agriculture B1.2 Motivation view" view defined in the "Agriculture
B1.2 modelling with Archimate" view point.
Figure 24: Agriculture pilot B1.2 Motivation view
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8.3.2 Agriculture pilot B1.2 Strategy view
This section describes the "Agriculture B1.2 Strategy view" view defined in the "Agriculture
B1.2 modelling with Archimate" view point.
Figure 25: Agriculture pilot B1.2 Strategy view
8.4 Pilot Evaluation Plan
8.4.1 High level goals and KPI's
Two relevant KPIs has been identified so far, namely:
• Reduction in the average cost of irrigation per hectare following the advisory services
at a given period.
• Decrease of inputs focused on irrigation (amount of water used)
8.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work packages 45 (WP4/5).
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Figure 26: Agriculture pilot B1.2 initial roadmap
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8.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.
Figure 27: Agriculture pilot B1.2 BDVA reference model
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Pilot 7 [B1.3] Cereal, biomass and cotton
crops_3 9.1 Pilot overview
9.1.1 Pilot introduction
This pilot aims mainly at implementing remote sensing and proximal sensor network big data
technologies for biomass crop monitoring, predictions, and management in order to
sustainably increase farming productivity and quality, while at the same time, minimizing
farming and environment associated risks. Biomass crops of interest include biomass
sorghum, fiber hemp and cardoon which can be used for several purposes including,
respectively, biofuel, fiber, and biochemicals, with a high macroeconomic impact. Producing
biofuel from plant biomass is an attractive alternative to fossil sources, not only because the
latter are non-renewable and environmentally harmful, but also because of the pressing issue
for nations to get independent of foreign energy sources. Several countries worldwide have
initiated programs to convert biomasses into biofuels and, in European countries, dedicated
biomass crops are being increasingly developed. The pilot is therefore well aligned with the
lines of action and the objectives and action plans of the bioenergy supply chains of Italy
(https://www.politicheagricole.it/) and the European Union. In 2011, the European
Commission included the biomasses for agroindustrial use among the six highly innovative
markets to be promoted in the near future; this concept was repeated by the Seventh
Framework Program for Research and Technological Development and by Horizon 2020
program.
9.1.2 Pilot overview
The pilot was designed to offer precision farming services consisting in different crop
monitoring and management technologies specifically geared to biomass crops with
particular interest in sorghum, fiber hemp and cardoon. Offered smart farming services
include (1) Biomass crop (sorghum/hemp/cardoon) monitoring using remote sensor and real-
time streaming sensor network big data, (2) crop growth and yield modelling, (3) early
warning: yield and quality deviation, threatening events (biotic/abiotic stress, threshold
alerts) for crop growth and development, (4) visualization: processed data and model results
are published in an intuitive way and viewable on the computer, smartphone or tablet, (5)
GPRS connectivity, (6) cloud based crop management infrastructure, (7) web portal cloud
solution. The pilot secured adhesion of fifteen farmers and/or farming cooperatives with
more than 120 ha. The pilot will run intensive extension services to promote the adoption of
the new technologies (remote sensing, IoT). CREA will work on sorghum and fiber hemp, while
Novamont will work on cardoon. VITO will support remote sensing technologies, while CREA
will support proximal streaming sensor network data technologies. The following table
provides an overview of the pilot activities.
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Table 19: Agriculture pilot B1.3 Overview of pilot activities
CREA sorghum pilot CREA fiber hemp
pilot
NOVAMONT
cardoon pilot
Location 24 sites in Emilia
Romagna, Italy
3 sites in Emilia
Romagna and
Veneto, Italy
4 sites in North and
South-Western
Sardinia, Italy
Area Size 120ha 6ha 65ha
End-Users CREA, farmers,
farming
cooperatives
CREA, single farmer NOVAMONT
Current Situation Crops are monitored based on visual observations, which is time
consuming. Furthermore, visual observations are not dependable
as they cannot allow accurate prediction of yields or the
identification of within field phenotypic variations.
Biomass sorghum, fiber hemp and cardoon were included in the pilots as motivated under
the above introduction section.
Method
This pilot will use satellite imagery (Sentinel-2) and/or telemetry IoT to monitor biomass crops
and predict yields. The pilots will be run by CREA and Novamont in collaboration with VITO.
Main crops will include sorghum and hemp for CREA, and Cardoon for Novamont. VITO will
use satellite data to monitor the crops and will develop yield models. Telemetry IoT
technology will be implemented by CREA on 5 sorghum piloting sites in Anzola experimental
station (Bologna, Italy).
Specifically, as part of pilot innovative solution, an online platform will be used to provide
satellite imagery, weather and soil data and yield predictions. VITO, in collaboration with a
number of Belgian partners, has developed a web application “WatchITgrow®” for potato
monitoring and yield prediction in Belgium. The existing WatchITgrow® application will
“filled” with satellite, weather and soil data for the Italian pilot sites. To be able to provide
yield forecasts for sorghum, hemp and cardoon developments are needed. The field data that
will be collected by CREA and Novamont will be input for setting up yield and phenology
models using machine learning techniques and will support continuous system learning.
The farmer and pilot owners can use the satellite imagery (biomass index, 10m resolution) to
monitor and benchmark their field productivity potential relative to production levels
achieved in the region. After one year, yield prediction can be implemented using the test
data from year one, and other historical data (when available).
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Telemetry IoT stations will transmit data to the cloud infrastructure in the process of crop
monitoring, biotic and abiotic stress diagnostic, alert and operational recommendations using
cloud-based crop management analytics including web portal cloud solution.
The telemetry IoT is designed to provide smart farming services. These include but are not
limited to remote monitoring capability (crop, soil and environmental properties), viewing (on
web, PC and mobile devices) and evaluating real-time data instantaneously for better decision
to improve yield and quality, conserve resources, and increase profits, cloud-based data
analysis to deliver summarized data to pilot manager (user), notify users via text and/or email
when critical thresholds have been breached. In the framework of this pilot, the telemetry
IoT will serve as a means to assess the usefulness of shifting from field-bound sensors data
towards cost-effective remote sensing (satellite data) solution.
Relevance to and availability of Big Data and Big Data infrastructure
Historic phenotypic data were recorded over more than thirty years in some crops
(Habyarimana et al., 2016). Telemetry IoT hardware and software were anticipated, with
GPRS connectivity, wireless sensor network (WSN), cloud based crop management
infrastructure, and web portal cloud solution. The WSN has a customizable time step for data
collection (1-60 minutes) and can collect big data: air temperature, air moisture, solar
radiation, leaf wetness, rainfall, wind speed and direction, soil moisture, soil temperature, soil
EC/salinity, PAR, and barometric pressure.
ESA’s high-frequency Sentinel satellites deliver a constant stream of information to identify
changes in crops and soil. The satellites provide cost-effective information on crop growth
and development every 5 days at field scale (up to 10m detail), which allows farmers,
agronomists, processing companies, to make better, more informed decisions.
Benefit of pilot
The availability of an online monitoring system whereby the farmer or agronomist has a global
view on the whole production area from space will facilitate the organization of field visits,
farm and business operations.
The pilot is expected to directly improve farm productivity and profits due not only to
increased yields, but also to the reduction of farming costs and risks. Productivity
improvements will mainly derive from: (1) timely efficient farming operations and responding
to biotic and abiotic stresses and IoT alerts, (2) rationalization of agricultural inputs, and (3)
accurate prediction models.
The results of this pilot will help undertake further extension services endeavours and
investigations to improve remote sensing and telemetry IoT technologies. The large sample
of piloting sites was anticipated to ensure that the proposed solutions can be replicated to
other crop types and market segments in the near future. Scientific papers along with large
amounts of data will produced and made FAIR (Findable, Accessible, Interoperable, Reusable)
to further science and knowledge of man.
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9.2 Pilot case definition
Table 20: Summary of pilot B1.3 (ISO JTC1 WG9 use case template)
Use case title Biomass crops monitoring and performance predictions
Vertical (area) Agriculture
Author/company/email 1. Ephrem Habyarimana / CREA / [email protected]
2. Sara Guerrini / Novamont / [email protected]
3. Isabelle Piccard / VITO / [email protected]
Actors/stakeholders and their roles and
responsibilities
CREA/Farmers Cooperatives, responsible for technological, farming and operational decisions.
Novamont/Farmers, responsible for technological, farming and operational decisions.
Agroindustry/consumer, responsible for the manufacturing quality, sustainability, the business push, and market pull.
Goals Using satellite imagery and/or telemetry IoT to monitor biomass crops and predict yields. Refer to the evaluation section for specific goals and KPIs.
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
Current
solutions
Compute(System) PCs, Laptops (CORE i7), standards OSs. Multi-year/environment data are processed and meta-analyzed. Vegetation indices, satellite and streaming telemetry IoT big data are not harnessed yet.
Storage Disk drives and SSDs + backup
Networking Social networks: LinkedIn, Facebook, Twitter
Software Multiple individual algorithm systems, not integrated processing and display.
Big data characteristics
Data source CREA will run 24 piloting sites (>120 ha) in Emilia Romagna, while Novamont will run 4 piloting sites (65 ha) in North and South-Western Sardinia, Italy.
An online platform will be used to provide satellite imagery with a 10m resolution (Sentinel 2A + B, possibly complemented with coarser
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resolution data and proximal sensor data to overcome cloud problems and provide ground-truth data), weather data, soil data and yield predictions.
Telemetry IoT will be designed to provide smart farming services. These include but are not limited to remote monitoring capability (crop, soil and environmental properties), viewing (on web, PC and mobile devices) and evaluating real-time data instantaneously for better decision to improve yield and quality, conserve resources, and increase profits, cloud-based data analysis to deliver summarized data to pilot manager (user), notify users via text and/or email when critical thresholds have been breached.
Volume (size) Hundreds of terabytes per year when all sources of data are considered.
Velocity
(e.g. real time)
Satellite data: Sentinel-2A+B images are acquired with a time step of 5 days. The images are pre-processed and distributed by ESA within 24 hours after acquisition. Further processing by VITO starts as soon as the images are available from ESA. Generally, the final information products become available for the end-users between 24 and 48 hours after image acquisition.
Telemetry IoT data: Time step for data collection is customizable, 1-60 minutes; big data: air temperature, air moisture, solar radiation, leaf wetness, rainfall, wind speed and direction, soil moisture, soil temperature, soil EC/salinity, PAR, barometric pressure.
Phenotypic data are collected each cropping season.
Variety
(multiple datasets, mashup)
Great variety. (1) Satellite: imagery, multispectral data, indices (soil, water, vegetation, biophysical), (2) Telemetry IOT: air temperature, air moisture, solar radiation, leaf
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wetness, rainfall, wind speed and direction, soil moisture, soil temperature, soil EC/salinity, PAR, barometric pressure. (3) analytics and phenotypic data.
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Need to have tools to produce and process ground-truth data for satellite data calibration.
Visualization Visualization of crop monitoring output at least bi-weekly during the cropping season, indices and predictions; real-time monitoring output, alerts, and recommendations.
Data quality (syntax) Data validity filtering w.r.t. completeness. Data fusion and modelling of heterogeneous data (EO data, telemetry IoT data, field data)
Data types Imagery, graphics, vector, numbers, analytical results, measurements, metadata, geolocations, spectra, time series.
Data analytics Predictive analytics for the development of data-driven yield models; predictive feedback (monitoring), real-time streaming data analytics to alert and provide operational recommendations using cloud-based crop management analytics including web portal cloud solution.
Big data specific challenges (Gaps)
There is a need for: (1) improving analytic and modelling systems that provide reliable and robust statistical estimated using large size of heterogeneous data; (2) closing the gap between agricultural business planning and the responsible and sustainable maximization of the profit deriving mainly from increased crop productivity and efficiency of resource use, reduced uncertainty of management decisions, accounting for environmental standards and regulations.
Big data specific challenges in bioeconomy
Delivering content and services to various computing platforms from
Windows desktops to Android and iOS mobile devices
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Security and privacy
technical considerations
Farm owner and geolocalization are highly sensitive, should be anonymized
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Real-time streaming data analytics and predictive analytics using machine learning for crop monitoring and developing yield models based on big data are universal solutions with domain agnostic applications.
More information (URLs)
www.databio.eu
<other URLs to be added later if relevant>
Note:
9.2.1 Stakeholder and user stories
The end users are farmers, farming cooperatives, agroindustry and research institutions. VITO
will use satellite data to monitor the crops and will develop yield models. Telemetry IoT
technology will be implemented by CREA to provide real-time analytics solutions. Combining
crop monitoring, yield models and real-time analytics will provide localized descriptive,
prescriptive and predictive plans to improve farming operations and productivity.
Stakeholders and user stories are summarized in the table below.
Table 21: Agriculture pilot B1.3 stakeholders and user stories
Stakeholders User story Motivation
Farmers, farming
cooperatives,
research
institutions
want to monitor the growth their
biomass crops throughout the
season
to evaluate the right time for
possible actions like fertilizing,
irrigation, crop protection, and
harvesting, optimize field
operations and save money and
time.
Farmers, farming
cooperatives,
research
institutions
want to evaluate differences in
plant growth between and within
fields
to evaluate possible actions like
fertilizing, irrigation, and crop
protection measures for specific
areas
Farmers, farming
cooperatives,
research
institutions
want to evaluate weather data,
planting date and yield for one or
several years
to explain yield differences and
evaluate possible tactical actions
for future seasons
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Farmers, farming
cooperatives
want better yield predictions to improve their processing
capacity, harvest and sales (stock
market) planning
Farmers, farming
cooperatives,
research
institutions
want empirical comparison
between remote and proximal
sensor-based solutions.
to identify the cost-effective
decision support tool.
9.2.2 Motivation and strategy
The main motivations for this pilot are:
● identifying the potential for using satellite data and machine learning for biomass crop
monitoring and the development of yield models.
● Evaluating the comparative importance between the use of proximal wireless sensor
network data and satellite data in biomass farm telemetry
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
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9.3 Pilot modelling with ArchiMate
9.3.1 Agriculture pilot B1.3 Motivation view
This section presents the "Agro Pilot B1.3 Motivation view" view defined in the "Agro Pilot
1.3.1 View Points" view point.
Figure 28: Agriculture pilot B1.3 Motivation view
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9.3.2 Agriculture pilot B1.3 Strategy view
This section presents the "Agro Pilot B1.3 Strategy view" view defined in the "Agro Pilot 1.3.1
View Points" view point.
Figure 29: Agriculture pilot B1.3 Strategy view
9.4 Pilot Evaluation Plan
9.4.1 High level goals and KPI's
Three relevant KPIs have been identified so far, namely:
• %Increase in farm productivity following the advisory irrigation, fertilization, pest/ disease management services vs what would be the revenue following standard farming practices based on historical data: Quantify %increase in farm productivity for all three crop types.
• %Decrease in operational costs for performing the same farming activities (through better management of resources) following the advisory irrigation, fertilization, pest/disease management services vs what would be the revenue following standard
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farming practices based on historical data: Quantify %Decrease in operational costs for all three crop types.
• %Decrease in fresh water and fertilizer usage following the aforementioned advisory services
9.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work packages 4 and 5 (WP4, WP5).
Figure 30: Agriculture pilot B1.3 initial roadmap
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9.5 Big data assets The diagrams below summarize Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component name.
Figure 31: Agriculture pilot B1.3 BDVA reference model for IoT
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Figure 32: Agriculture pilot B1.3 BDVA reference model for Satellite data
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Pilot 8 [B1.4] Cereal, biomass and cotton
crops_4 10.1 Pilot overview
10.1.1 Pilot introduction
The pilot aims to develop a platform for mapping of crop vigor status by using EO data
(Landsat, Sentinel) as the support tool for variable rate application (VRA) of fertilizers and
crop protection. This includes identification of crop status, mapping of spatial variability and
delineation of management zones.
The Czech Republic has a specific land use defined by the highest average holding area in EU
(over 130 ha per farm). The national statistical report of agriculture sector ("Green Report
2015", Ministry of agriculture, Czech Republic) shows that there are 637 farms with acreage
of managed land over 1000 ha which together cultivate 50.3% of agricultural land in Czech
Republic. Also, there is known higher average size of fields. Statistical evaluation of the size
of land parcels in LPIS shows that 60% of arable land is located within the fields with the area
over 20 hectares. Higher diversity of the relief and pedoclimatic conditions in combination
with the size of land blocks occur in visible heterogeneity of land. This leads to an increased
interest in the precision farming practices and technologies for site-specific crop
management, where remote sensing (EO) plays a crucial role.
The pilot farm Rostenice a.s. with 8.300 ha of arable land represents a bigger enterprise
established by aggregating several farms in past 20 years. Main production is focused on the
cereals (winter wheat, spring barley, grain maize), oilseed rape and silage maize for biogas
power station. Crop cultivation is under standard practices, partly conservation practices is
treated on the sloped fields threatened by soil erosion. Over 1600 ha is mapped since 2006
by high density soil sampling (1 sample per 3 ha) as the input information for variable
application of base fertilizers (P, K, Mg, Ca). Farm machines are equipped by RTK guidance
with 2-4 cm accuracy. Farm agronomists don’t use any strategy for VRA of nitrogen fertilizers
and crop protection because of lack of reliable solutions in CZ.
10.1.2 Pilot overview
The main focus of the pilot will be on the monitoring of cereal fields by high resolution satellite
imaging data (Landsat 8, Sentinel 2) and delineation of management zones within the fields
for variable rate application of fertilizers. The main innovation is to offer a solution in form of
web GIS portal for farmers, where users could monitor their fields from EO data based on the
specified time period, select cloudless scenes and use them for further analysis. This analysis
includes unsupervised classification for defined number of classes as identification of main
zones and generating prescription maps for variable rate application of fertilizers or crop
protection products based on the mean doses defined by farmers in web GIS interface.
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10.2 Pilot case definition
Table 22: Summary of pilot B1.4 (ISO JTC1 WG9 use case template)
Use case title Cereal crop monitoring (Pilot B1.4_CZ_LESPRO)
Location Rostenice (Vyskov, Czech Republic)
Area Size 8,300 ha
Target Crops Cereals (winter wheat, spring barley, grain maize)
Goals Develop a platform for mapping of crop vigor status by using EO data (Landsat, Sentinel)
Use case description
Development of Web Map Service / Portal for visualization, analysis and processing of EO data over farm fields as the support tool for creating prescription maps for VRA (mainly application of N fertilizers or crop protection).
Current
solutions
Compute(System) Dedicated server
Intel® Xeon® E5-2420 v2, 2.20GHz, 6 cores, 64GB ECC RAM
Storage HDD: 2x 4TB + 2x 8TB (12 TB total)
Networking -
Software -
Big data characteristics
Data source (distributed/centralized)
Centralized:
Landsat data repository (https://espa.cr.usgs.gov)
Sentinel 2A/B data source (https://scihub.copernicus.eu/)
Google Earth Engine platform for fast viewing EO data:
(https://earthengine.google.com/)
Field boundaries from Czech LPIS database as shp or xml (http://eagri.cz/public/app/eagriapp/lpisdata/)
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Ortophotos, topography maps, cadastral maps – as WMS service (http://geoportal.cuzk.cz/)
Farm data:
Crop rotation, crop treatments records, yield maps, soil maps
Volume (size) EO data – hundreds of GB per year for farm area (one Sentinel 2 tile, two Landsat tiles)
LPIS – 0.5 Gb for Czech Rep. as actual data, for pilot area up to 200 MB including historical data (from 2004)
Farm data – yield maps up to 1GB per year (point + raster data). Treatments records as tables.
Velocity
(e.g. real time)
Sentinel 2 approx. 4 days revisit time
Landsat 8 16 days revisit time (resp. 8 days)
LPIS changes – monthly to once per year
Farm data -
Variety
(multiple datasets, mashup)
EO data is acquired as raster format (geotiff), LPIS as xml/shp vector data.
Farm data as shp, geotiff, dbf and other
Variability (rate of change)
EO data 2-3 days during vegetation season (March-July), once a week in the rest of the year.
Farm data are updated irregularly.
Big data science (collection,
curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Centralized data are trusted: EO data - European Space Agency (ESA) and United States Geological Survey (USGS). LPIS data Ministry of Agriculture of Czech Republic.
Visualization Standard visualization service for raster and vector data. WMS visualization defined by providers (Czech Office for Surveying, Mapping and Cadastre)
Data quality (syntax) Data quality depends on the providers
Data types Geotiff, shp, xml, dbf and other
Data analytics
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Big data specific challenges
(Gaps)
Combining EO data with farm geospatial records.
Big data specific challenges in bioeconomy
Identification of crop growing parameters and its classification by multitemporal analysis of high resolution (spatial and temporal) EO data. Transferability of results and processing workflow across various field and crop management condition.
Security and privacy
technical considerations
No datasets of this pilot are considered sensitive or related to personal data.
Highlight issues for generalizing
this Use case (e.g. for ref.
architecture)
Main technical problems are related to automatization of EO data processing and storage.
More information
(URLs)
Landsat ESPA https://espa.cr.usgs.gov
ESA https://scihub.copernicus.eu/
Google Earth Engine https://earthengine.google.com/
Czech LPIS (public) http://eagri.cz/public/app/lpisext/lpis/verejny2/plpis/
Note: <additional comments>
10.2.1 Stakeholder and user stories
Table 23: Agriculture pilot B1.4 stakeholders and user stories
Who (type of user) I want to (can you perform some
task)
Why (achieve some goals)
Farm Enterprise,
Agronomist, Farm
Advisor, Service Dealer
want to monitor the growth of
cereal crops during the season
to evaluate possible actions like
fertilizing and crop health
measures
Farm Enterprise,
Agronomist, Farm
Advisor, Service Dealer
want to evaluate differences in
plant growth between important
crop growth stages
to evaluate possible actions like
fertilizing and crop health
measures for specific areas
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Farm Enterprise,
Agronomist, Farm
Advisor, Service Dealer
want to analyse crop biomass and
yield variability within each field
for one or several years.
to explain yield differences and
evaluate possible tactical
actions for future seasons
Farm Enterprise,
Agronomist, Farm
Advisor, Service Dealer
want to classify management
zones and to estimate crop
treatment intensity
to prepare prescription maps
for fertilizing and crop
protection
10.2.2 Motivation and strategy
Agronomists want effective management of agrochemicals (fertilizers, pesticides) in form of
site specific crop management treatments and improved farm productivity while following
sustainable agriculture practices.
Our solution is to develop web map portal (service) for visualization, analysis and processing
of EO data for selected farm area:
● Visualization of crop status by vegetation indices within selected time period
● Identification of spatial variability of crops within each farm field and alerting service
for farmers (in case that crop variability is higher than usually for that site)
● Zoning of fields by using machine learning algorithms for selected time-period and
creating of prescription maps (incl. export into shp or isoxml) by considering restricted
zones for application.
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10.3 Pilot modelling with ArchiMate
10.3.1 Agriculture pilot B1.4 Motivation view
This section presents the "Agro Pilot B1.4 Motivation view" view defined in the "Agro Pilot
1.3.1 View Points" view point.
Figure 33: Agriculture pilot B1.4 Motivation view
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10.3.2 Agriculture pilot B1.4 Strategy view
This section presents the "Agro Pilot B1.4 Strategy view" view defined in the "Agro Pilot 1.3.1
View Points" view point.
Figure 34: Agriculture pilot B1.4 Strategy view
10.4 Pilot Evaluation Plan
10.4.1 High level goals and KPI's
Three relevant KPI were identified for this pilot:
• Area of processed EO data
• Accuracy of management zones delineation by field survey and yield maps
• Increase of fertilizers use efficiency and farm productivity
10.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work packages 4 and 5 (WP4, WP5).
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Figure 35: Agriculture pilot B1.4 initial roadmap
10.5 Big data assets
Figure 36: Agriculture pilot B1.4 BDVA reference model
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Pilot 9 [B2.1] Machinery management 11.1 Pilot overview
11.1.1 Pilot introduction
This pilot is focused mainly on collecting telemetry data from machinery and analysing them
in relation with other farm data. The main challenge is access to data and data integration,
when farmer uses tractors and equipment from various manufacturers with different
telematics solutions and different data ownership/sharing policy. Czech tractor manufacturer
Zetor that is now developing and implementing first telematics system for its tractors will
cooperate on creating interoperable solution for access to tractor data, FederUnacoma will
support the pilot with its huge experience in electronic communication standards and in AEF
project teams working on M2M synchronization, wireless infield communication and FMIS
systems. Lesprojekt will ensure integration with other relevant farm data and related analysis.
11.1.2 Pilot overview
In many cases farms or agriculture service organizations owns tractors of more than one
brand/family. Although the communication protocols used in control units of farm machinery
and data collection are subject of standardization, the telematics solutions including data
ownership/usage policy are usually specific to each tractor brand/family and the level.
Furthermore, attention shall be paid to ISO and CEN standards regulating data sharing in
agriculture basing on the input coming from industry organizations like CEMA and AEF.
Although this is not issue and can be even desirable for purposes of tractor producer’s
customer care responsible for solving technical problems on tractor, for farmers it can be hard
or impossible to connect the data coming from tractor with other farm data relevant for
agronomical / economical evaluation of machinery usage. Despite the fact tractor have
telematics solution, farmer sometimes need to use third party device and software to obtain
data for field specific analysis
Method
Zetor company is currently developing and testing modular telematics solution which is
supposed to be part of all Zetor tractors. The solution will provide several levels of
functionality ranging from basic telematics for customer care and basic location information
for customer to field specific economic analysis and precision agriculture.
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Figure 37: Zetor tractors
The highest level of modular solution will offer connection to other data relevant for farm
management like field boundaries obtained Land Parcel Information system (LPIS), elevation
model and possibly yield potential maps derived from EO data. This connection will enable
evaluation of economic efficiency and other analysis on the level of individual fields or even
parts of the field.
These analyses will become part of Zetor solution and/or they will be enabled by defining data
exchange policy and opening subset of telematics data to third party farm management
information systems and analytical services.
Lesprojekt is member of Wirelessinfo association and participates on development of system
for monitoring and analysis of farm machinery data. The data are obtained using third party
monitoring units installed to tractors but the system is ready to accept data from build-in
telematics system if tractor manufacturer opens the system to external services. The analysis
is focused mainly on evaluation of economic efficiency of machinery usage and it enables
analysis on the level of individual tractors, fields or parts of the field (Management Zones).
Lesprojekt will cooperate with Zetor on the development and testing of telematics solution.
FederUnacoma association which is expert in standardization in agricultural industry will
provide support and advisory services for this pilot.
Relevance to and availability of Big Data and Big Data infrastructure
This pilot has two main connections to big data.
1. Management zones reflecting in field variability, which are going to be connected with machinery data which are based on EO data covering vegetation period through several years (up to 8 years). Tools for automatic selection of suitable scenes and EO data processing chain are needed.
2. Data coming from machinery can reach hundreds of MB per one tractor per year. Total volume is multiplied by number of tractors.
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11.2 Pilot case definition
Table 24: Summary of pilot B2.1 (ISO JTC1 WG9 use case template)
Use case title Machinery management
Vertical (area) Agriculture
Author/company/email Karel Charvát jr./ Lesprojekt /[email protected]
Jaroslav Šmejkal/ Zetor/ [email protected]
Alessio Bolognesi/ FederUnacoma /
Actors/stakeholders and
their roles and
responsibilities
Farmers, Advisory services, Tractor manufacturers
Goals ● Support development and implementation of Zetor’s
telematics solution
● Support authorized users’ access to telematics data using
tractor manufacturer’s build-in telematics solution or
third-party monitoring tools (depends on tractor
manufacturer data ownership/usage policy.)
● Support integration of tractor and agricultural machinery
data with other relevant farm data and interfaces for data
import into FMIS
● Extracting comparable information from data coming from
various tractors of different manufacturers
● Evaluation of economic efficiency of tractor/machinery
usage and crop profitability.
Use case description Refer to the pilot case definition section and diagrams in the pilot
modelling sections.
Current
solutions
Compute(System) Individual systems of different tractor
manufacturers/families. Various FMIS
(farm management information
systems)
Storage Various telematics system and FMIS
systems
Networking Various
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Software Various
Big data
characteristics
Data source
(distributed/centralized)
EO: centralized
Farm data: distributed
Tractor data: distributed
Volume (size) Tractor data: megabytes per each
tractor per week
Field boundaries: megabytes per
farm, gigabytes per country
Other farm data: Up to gigabyte per
year
EO data – hundreds of GB per year for farm area (Czech Republic)
Management zones based on EO data:
megabytes per farm per year
Velocity
(e.g. real time)
Tractor data: Real time
Field boundaries: typically one per
year
Other farm data: irregularly, based on
data type
EO data: Landsat8 every 16 days,
Sentinel 2 every 4 days
Variety
(multiple datasets,
mashup)
Multiple datasets
Variability (rate of
change)
High
Big data science
(collection, curation,
analysis,
Veracity (Robustness
Issues, semantics)
Data coming from various tractor
types or various monitoring units (e.g.
fuel consumption) can have different
interpretation. Sometimes
recalculation is necessary to make
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action) data from different tractors
comparable
Visualization Web based maps (e.g. HSLayers) using
various layers: background map, field
polygons, management zones,
tractors trajectory.
Graphs shoving results of various
analysis related to tractor utilization
or operation cost.
Data quality (syntax) Various datasets have various quality.
Quality of tractor data depends on
tractor type, build in telematics
solution or third-party monitoring
unit.
Quality of field boundaries data varies
between countries and sometimes
between farms.
EO data quality is changing over time.
Data types Imagery,
Machinery data in various formats,
Field data and management zones
data in vector formats + related
attribute data
Data analytics Descriptive analytics focused on
tractor utilization, economic
efficiency, and in-field variability
related to management zones.
Predictive analytics: Detection and
prediction failures in Zetor tractors.
Big data specific
challenges (Gaps)
EO data selection, processing, management zones delineation and
integration with tractors monitoring require manual inputs. There
is a need for reduction of manual inputs.
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Big data specific
challenges in
bioeconomy
Using big data technologies to improve energy efficiency, protect
soil and environment, reduce using chemicals
Security and privacy
technical considerations
Telematics data, visualisations, and analysis must be accessible
only to authorized person
Highlight issues for
generalizing this Use
case (e.g. for ref.
architecture)
EO data management might be at could have many similarities
with other pilots
More information (URLs) www.databio.eu
Note: <additional comments>
11.2.1 Stakeholder and user stories
Table 25: Agriculture pilot B2.1 stakeholders and user stories
Who (type of
user)
I want to (can you perform some task) Why (achieve some goals)
Zetor company Implement telematics solution for
their tractors which offers access to
tractor data to authorized users
(farmers, customer care) and
interoperability with other farm
related data, FMIS etc.
Offer added value to customers.
Farmer/advisors Track my tractors which might be
made by different manufacturers and
analyse their utilization in relation
with other farm data or id-field
variability data based on EO.
Have an overview of the location
and movement of tractors.
Evaluate economic efficiency of
machinery usage and crops
profitability, including in.
Optimize cultivation technology
11.2.2 Motivation and strategy
● The main motivation for farmers/advisors is using single environment to analyse and
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display data coming from various tractor in relation with other farm and external data.
This analysis can support decisions affecting economic efficiency.
● The main motivation for Zetor is implementation of telematics solution, which satisfy
the user need and helps to improve customer care.
11.3 Pilot modelling with ArchiMate
11.3.1 Agriculture pilot B2.1 Motivation view
This section presents the "Agro B2.1 Motivation view" view defined in the "Agro Pilot 1.3.1
View Points" view point.
Figure 38: Agriculture pilot B2.1 Motivation view
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11.3.2 Agriculture Pilot B2.1 Strategy view
This section provides the "Agro Pilot B2.1 Strategy view" view defined in the "Agro Pilot 1.3.1
View Points" view point.
Figure 39: Agriculture pilot B2.1 Strategy view
11.4 Pilot Evaluation Plan
11.4.1 High level goals and KPI's
These relevant KPIs have been identified so far:
• Numbers of tractors and agricultural machinery using DataBio solutions.
• Number of various tractor brand/models tested.
• Amount of collected data.
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11.4.2 Initial roadmap
Figure 40: Agriculture Pilot B2.1 initial roadmap
11.5 Big data assets
Figure 41: Agriculture pilot B2.1 Strategy view
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Pilot 10 [C1.1] Insurance (Greece) 12.1 Pilot overview
12.1.1 Pilot introduction
The agri-food sector is constantly exposed to major risks threatening its viability. Production
risks are among the biggest concerns of the agribusiness value chain as they relate to the
uncertainty about the production levels that the farmers could reach following standard
farming practices. The agricultural sector is extremely vulnerable to physical hazards (e.g.,
floods, hail) and biological threats (e.g., pests, diseases). Thereby, insurance in the agri-food
sector deals with the increasing demand for agricultural insurance products and is expected
to play a vital role in the forthcoming years as a tool for risk management. However, due to
its multi-parametric nature, agricultural insurance is considered a special category in the
insurance product portfolio. Difficulties in obtaining enough and valuable data for damage
assessment, the complex biological processes that are incorporated in the crops growth
stages, and the vast variability of production according to geographical criteria, creates an
environment of great uncertainty that requires new techniques and expert knowledge.
12.1.2 Pilot overview
The main focus of the proposed pilot is to evaluate a set of tools and services dedicated for
the agriculture insurance market that aims to eliminate the need for on-the-spot checks for
damage assessment. The pilot will concentrate on fusing heterogeneous data (EO data, field
data) for the assessment of damages at field level. NP will support the activities for the
execution of the full life-cycle of the pilot. Moreover, a major Greek insurance company,
Interamerican, will be actively engaged in the pilot activities, bringing critical insights and its
long standing expertise into fine-tuning and shaping the technological tools to be offered to
the agriculture insurance market.
Table 26: Agriculture pilot C1.1 overview of pilot activities
Location North Greece
Area Size 12000ha
Targeted Crops 7 crop types (wheat, stone fruits, etc.)
End-Users Insurance value chain stakeholders
Method
The overall objective of the pilot is to validate a holistic framework comprising of EO-based
data fused with field measurements from IoT stations and offered through flexible UIs and
information visualization mechanisms. The system is designed appropriately to support key
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business processes and the need of the insurance market value chain stakeholders. The
methodology of the pilot activities involves the integration of high power computing, machine
learning methods, geospatial data analytics with data coming from EO platforms and will
allow for integration with IoT based data streams and services. The convergence of the
aforementioned technologies in a single dedicated framework is expected to deal effectively
with insurance market demands which require a smooth transition from traditional insurance
policies (expensive, require human experts for damage assessment) to more flexible index-
based insurances. Index products are principally oriented in investigating yield loss due to
extreme events throughout the cultivating period, e.g. rainfall deficit/excess or high/low
temperature. Most of the developed indices are crop-specific divided into fractions of crop
life cycle and calibrated using historical yield statistics [REF-09]. This way, index-based
insurance provides transparency and reduces bureaucracy since it is based on objective
predefined thresholds. Furthermore, it has low operational costs requiring minimal human
intervention. On the top of that, this new type of insurance can eliminate field loss
assessment, adverse selection and moral hazards since the whole process is fully automated,
meaning that the point where the pay-out starts (trigger) and the point where the maximum
pay-out is reached (exit) are based on a prespecified fixed model per crop. This pilot can
drastically contribute towards the aforementioned directions and the generation of index
insurance products via the IoT stations network, which can provide historical and current
weather data, enriched with yield data information extracted from the work calendar and
stored in the NP’s cloud infrastructure. On the other hand, satellites can provide crucial
information to support insurance by overcoming challenges related to lack of stations in all
insured areas or unavailability of long-time series data. This way, EO data can be exploited for
damage assessment (against weather risks) and damage frequency over years as well as to
create value propositions for the key stakeholders of the insurance industry (Interamerican).
Relevance to and availability of Big Data and Big Data infrastructure
Within its cloud infrastructure (GAIA cloud), NP has already started collecting remote sensing
data from the new Sentinel 2 optical products which are being extracted and stored since the
start of 2016. This comprises both raw and processed data (atmospherically corrected
products, extracted vegetation indices) represented in raster formats that are being handled
and distributed using optimal big data management methodologies. Moreover, NP collects
field-sensing data through its network of telemetric IoT stations, called GAIAtrons. GAIAtrons
offer configurable data collection and transmission rates. Since 01/03/2016 over 1M samples
have been collected and stored to NP’s cloud infrastructure that refer to atmospheric and soil
measurements from various agricultural areas of Greece.
Benefit of pilot
The pilot is expected to have a direct impact on key business processes of the insurance
industry. A set of benefits include a) reduce bureaucracy, transparency, b) eliminate the need
for on-the-spot checks for damage assessment, c) eliminate adverse selection, d) eliminate
moral hazard, e) reduce operational and transaction costs, f) offer rapid pay-out. In addition,
it would lead to improvements in a) NP’s GAIA cloud’s stability, availability, security,
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interoperability and overall maturity, b) NP’s GAIABus DataSmart functionality in terms of
real-time analytics, data stream and decision support processes, multi-temporal object-based
monitoring, cloud-based services that integrate earth observation with image processing,
machine learning and spatial modelling. Moreover, it will allow new technological trends,
such as deep networks, to be exploited in order to facilitate and improve long-established
insurance procedures.
12.2 Pilot case definition
Table 27: Summary of pilot C1.1 (ISO JTC1 WG9 use case template)
Use case title Insurance
Vertical (area) Agriculture
Author/company/email NP
Actors/stakeholders and their roles and
responsibilities
Interamerican – Insurance company,
Single Farmers – insuring their crops against risks of various types.
Goals Provide damage assessment information through an automated holistic framework. Better control insurance claims and shape the insurance products (data abundancy, law of large numbers)
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
Current
solutions
Compute(System) Insurance companies offering products to farmers do not rely on additional information (satellite data, sensor data, etc.) for claim assessment. Instead, they most often offer rely on on-the-spot checks that require significant resources for the calculation of damages against weather (e.g. rainfall, temperature) and biological (e.g. pests, diseases, contamination) perils.
Storage -
Networking -
Software -
Big data characteristics
Data source (distributed/centralized)
Centralized (Within GAIA Cloud): Field sensing data from GAIAtrons, Remote sensing (Earth observation) data, anonymized IASC data
Volume (size) ~7.5 TB/year for remote sensing data, including raw data and extracted
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biophysical and vegetation indices for the pilot areas, ~5GB/year for field sensing data for 27 deployed GAIAtrons
Velocity
(e.g. real time)
Configurable data transmission for field sensing (a new set of measurements is being sent every 10 minutes in present configuration). Every 10 days new EO products available. Within 2018 EO products will be available every 5 days.
Variety
(multiple datasets, mashup)
Field Sensing: Soil temperature, humidity (multi-depth), ambient temperature, humidity, barometric pressure, solar radiation, leaf wetness, rainfall volume, wind speed and direction
Remote Sensing: 13 spectral bands
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Need for a system that can constantly provide support to key business processes of the insurance market
Visualization Spatio-temporal information for visually assessing the risk/damage level
Data quality (syntax) The quality of field sensing data is being controlled by several filtering, outlier detection and stream processing mechanisms. The integrity of remote sensing data quality is being assessed by a hash check upon product download.
Data types Remote sensing data provided in raster format (.jp2). Field sensing data provided as time series unstructured data with configurable frequency
Data analytics Diagnostic and descriptive
Big data specific challenges (Gaps)
There is a need for VHR data for validation and optimization of methodologies. There is a need for smarter fusion of the heterogeneous data types that are being collected towards providing accurate insights. To this end, it is important to explore
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mechanisms that could combine raster and vector data at parcel level (polygon) and station level (point).
Big data specific challenges in bioeconomy
It has been identified that technological trends, such as deep learning techniques, can be exploited for encountering several big data challenges (e.g. data-driven crop classification models, selection of training data sets per crops) and for advancing the effectiveness of traditional machine learning methodologies for the identification of possible crop damages/losses.
Security and privacy
technical considerations
A system intended to collect data from field sensors, installed in remote locations, is definitely going to face network connectivity challenges. In order to provide up-to-date and relevant advices, the system should be able to exhibit high availability and accuracy in its sensor readings and transmission mechanisms. Moreover, field sensing data should be securely transmitted to the cloud infrastructure and protected against various types of attacks that might set the system at risk.
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Machine learning methodology for crop modelling
More information (URLs)
https://www.interamerican.gr/
12.2.1 Stakeholder and user stories
Table 28: Agriculture pilot C1.1 stakeholders and user stories
Stakeholders User story Motivation
Farmer As a farmer I want to be insured against several types of risks (damages)
Be assured that I will be compensated in case of hazards for my crops
Insurance company (Interamerican)
As an insurance company I want to improve key business processes
Reduce operational costs related to damage assessment and collect abundant data in order to manage insurance claims more effectively
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12.2.2 Motivation and strategy
The main motivation for this pilot is:
• to raise the awareness of the farmers, farmer cooperatives and insurance companies on how new technological tools could optimize farm profitability (by insuring the agricultural products) and offer a significant advantage on a highly competitive sector.
• to promote the usage of EO-data, IoT data, etc. for better risk management and support in respect to key business processes (visual/field inspection of loss claims) that require time and extra expenses.
• to increase the technological capacity of the involved partners through a set of pilot activities that involves management of big data for high value crops.
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
12.3 Pilot modelling with ArchiMate The current section presents the "Agriculture C1.1 Insurance Greece modelling with
ArchiMate" view point described using the ArchiMate standard.
12.3.1 Agriculture pilot C1.1 Motivation view
This section presents the "Agriculture C1.1 Motivation view" view defined in the "Agriculture
C1.1 Insurance Greece modelling with ArchiMate" view point.
Figure 42: Agriculture pilot C1.1 Motivation view
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Motivation elements are used to model the motivations, or reasons, that guide the design or
change of an Enterprise Architecture. It is essential to understand the factors, often referred
to as drivers, which influence other motivation elements. They can originate from either
inside or outside the enterprise. Internal drivers, also called concerns, are associated with
stakeholders, which can be some individual human being or some group of human beings,
such as a project team, enterprise, or society. Examples of such internal drivers are customer
satisfaction, compliance to legislation, or profitability.
12.3.2 Agriculture C1.1 Strategy view
This section provides the "Agriculture C1.1 Strategy view" view defined in the "Agriculture
C1.1 Insurance Greece modelling with ArchiMate" view point.
Figure 43: Agriculture pilot C1.1 Strategy view
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The immediate decision support system is built on top of a data collection and distribution
system. The data collection and distribution system is used to collect sensor data from the
on-board systems and makes them available in a single system. The data distribution system
ensures that the decision support system only interface with a single system, instead of
multiple sensors. The decision support system presents the data from the data distribution
system and collect them in an internal storage system for presentation of current
performance vs. historic performance.
12.4 Pilot Evaluation Plan
12.4.1 High level goals and KPI's
One relevant KPI has been identified so far, namely:
• %Accuracy in damage assessment.
• %Decrease in the required time for conducting an assessment.
12.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 (WP4).
Figure 44: Agriculture pilot C1.1 initial roadmap
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12.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.
Figure 45: Agriculture pilot C1.1 BDVA reference model
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Pilot 11 [C1.2] Farm Weather Insurance
Assessment 13.1 Pilot overview
13.1.1 Pilot introduction
Agricultural production faces a myriad of risks. Nevertheless, two major risks are of concern
to the agricultural sector—price risk caused by potential volatility in prices and production
risk resulting from uncertainty about the levels of production that primary producers can
achieve from their current activities. It is likely that these major risks will increase in the
future—price risk due to liberalization of trade and production risk caused by the effects of
climate change. In this challenge environment, agricultural insurance is an important part of
ensuring long-term stability and growth of the agriculture sector, and facilitating access to
credit, helping to reduce the negative impacts of natural catastrophes, and encouraging
investment in innovative production techniques and technologies.
13.1.2 Pilot overview
The objective of proposed pilot is the provision and assessment on a test area of services for
agriculture insurance market, based on the usage of Copernicus satellite data series also
integrated with meteorological data, and other ground available data.
Among the needs of the insurances operating in agriculture, one of the most promising in
terms of fulfilment with Earth Observation data is the evaluation of risk assessment and
damages estimation down to parcel level. For damage assessment, the operational adoption
of remotely sensed data based services will allow optimization and tuning of new insurance
products based on objective parameters, such as maps and indices, derived from EO data and
allowing a strong reduction of ground surveys, with positive impact on insurances costs and
reduction of premium to be paid by the farmers.
For the risk assessment phase, the integrated usage of historical meteo series and satellite
derived indices, supported by proper modelling, will allow to tune EO based products in
support to the risk estimation phase.
Services provided in the pilot will support:
• Risk assessment: service of historical spectral and meteorological analysis in support to risk assessment of crop failures
• Damage and loss assessment: provision of indexes through the integrated usage of meteorological data and multispectral and multi-temporal information for the identification of site-critical weather situations on a small reference area.
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Table 29: Summary of pilot C1.2 (ISO JTC1 WG9 use case template)
Use case title
Farm Weather Insurance Assessment
Vertical (area) Agriculture
Author/company/email Coordinator: Antonella Catucci/e-GEOS/[email protected]
Actors/stakeholders and their roles and
responsibilities
Satellite Service Providers and Research and technology Organization/ Added value maps and products providing information for risk and damage assessment to be used by insurances in the agriculture domain;
Meteorological and Environmental EO service provider/ Meteorological data and value-added product about the historical and actual status of the considered areas of interest;
End Users/ definition of requirements/provision of input crop data/ validation of the service.
Goals The objective of proposed pilot is the provision and assessment on a test area of services for agriculture insurance market (risk and damage assessment useful for premium and reimbursement definition), based on the usage of Copernicus satellite data series also integrated with meteorological data, and other ground available data.
Use case description
Current
solutions
Compute(System) Non-existing system today. Traditional methods to assess the risk are related to statistical data on the insured parcels; while traditional methods for damage assessment and reimbursement payment are based on in field verification.
Storage Local system + web based information systems for general statistical data
Networking Manual assessment of Web, internal data about insured parcels
Software Multiple individual systems, not integrated processing and display
Big data characteristics
Data source (distributed/centralized)
Combination of both types:
Centralized – related to crop information and in-situ data
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Distributed/local: satellite meteorological and optical data
Volume (size) Meteorological: Each single data collection (e.g. precipitation from Meteosat) has a size of 3 MB per 100Km2 per 10 years considering a resolution of 4,5x4,5 km and frequency of 15 min
HR Optical S2 data: Each tile ~100x100km2 and ~0.5GB. 1 single tile cover each Pilot areas.
In situ data: Usually the local data can be estimated in 3 MB per year per 100km2 of interpolation area.
Velocity
(e.g. real time)
Not highly varying considering that: risk information is mainly related to historical data and actual but no-real time data. Damage assessment requires processing results after one two days after the event.
Variety
(multiple datasets, mashup)
Multiple Datasets: the idea is to include satellite meteorological optical data together with in-situ data provided by the users (past events, crop and soil information).
Variability (rate of change)
Rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Time-series analysis must contain a certain number of real measurement in order to give hints on the identified risks and damages.
Visualization Visualization of complex risk maps and processed images coupled with data analytics providing information about crop trends and eventual correlation results. Farmers (and insurance companies) are not used to analyse meteorological data in a geographic/spatially-distributed way. This underlines the need for a good visualisation component.
Data quality (syntax) Collected information must be linked to parcels database
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Data types Need to analyse multi-dimension (explore) and multisource data.
Data analytics Aggregation mechanism (Drill Down at parcel level (polygon) and station level (point).
Plotting and visualization of processing results (multi modal dashboard).
Big data specific challenges (Gaps)
There is a need for closing the gap between insurance daily activities and technological instruments able to provide historical and actual information able to support and optimize premium and reimbursement definition. Other need is to use Big Data techniques to investigate eventual correlation among weather data and crop damage in order to better estimate the spatial and temporal risk distribution.
Big data specific challenges in bio-
economy
The Earth observation and weather data will be challenging in sense of dealing with the Volume of data. Whereas the farm data will be challenging in Variety of data coming from different sources, in different formats, using different semantics.
Security and privacy
technical considerations
Reference data about loss from insurers or farmers will be used.
This would require a clause on confidentiality and a software
component handling security and privacy of certain datasets.
Moreover, the confidentiality of the services for the Insurance
could be an issue.
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Correlation analysis of different and heterogeneous datasets (actual and historical data) as support to the risk assessment. Damage estimation from image analysis by using machine learning approach and techniques.
More information (URLs)
www.databio.eu
<other URLs to be added later if relevant>
Note: <additional comments>
13.2 Pilot case definition The main end users are insurance acting in the agriculture domain. Nevertheless, farmers
have been considered as secondary users and beneficiaries of the services included in the
pilots. With regard to the Insurance activities two are the identified main components of the
insurance value chain that will be supported by the pilot activities:
• Premium definition: traditionally insurance estimation of the risks affecting the area, and linked to the premium requested, is realized by using a statistical approach. Ground true data together with Earth Observation and Meteorological information
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collected and correlated with Big Data advanced methods will drastically increase the risk assessment capacity and will support a more appropriate definition of the premium.
• Damage assessment: the damage assessment process to pay indemnities requests is traditionally conducted by means of in field inspections. The use of Big Data instruments and techniques that integrate different data sources will support the insurance also in this phase reducing the in-field management costs. Here following some use cases derived from the above-mentioned activities.
13.2.1 Stakeholder and user stories
Table 30: Agriculture pilot C1.2 stakeholders and user stories
Who (type of user) I want to (can you perform
some task)
Why (achieve some goals)
Insurance company determine the regional
spreading of risks for each
type of bad weather (hail,
heavy rain, drought)
to evaluate their insurance
portfolio
Insurance company determine temporal trend for
each type of bad weather
(hail, heavy rain, drought)
to determine the possible
influence of climate change
on crop growth.
Insurance company determine the actual risk per
crop on field level
to determine the pricing of
the insurance package
Insurance company assess the damage caused by
a bad weather event
ensure non-erroneous
compensation to farmers
Farmers view the risk level for heavy
rain and drought on field level
(optionally crop specific)
to evaluate the business case
for prevention measures
13.2.2 Motivation and strategy
Insurances need a two-step process of risk assessment, followed by damage assessment.
Relying on historical data of calamities (e.g. hail damage, frost damage, heavy rains),
(historical) meteorological data, soil data, crop data and height data a Big Data analysis
algorithm should provide the optimized risks pricing of insurance packages. The damage
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assessment will be relying on satellite imagery (providing biomass indices and/or weather
event extend maps) before and after the bad weather event to ensure non-erroneous
compensation process. These data should be combined with insurance data on damages to
calibrated a Big Data analysis algorithm.
The risk is based on:
● Local/regional weather and the frequency and intensity of bad weather conditions
(extreme rainfall, hail, drought)
● Sensitivity of the crop for the weather-related risks
● Topography (height / height differences) and
● Soil type
For the risk assessment, the geographic distribution of the risk areas will be presented, based
on a.o. historical (bad) weather conditions, like intense rainfall, hail and drought. It should
also be possible to present a temporal trend in order to determine the possible influence of
climate change on crop growth. For damage assessment satellite imagery (combined with
meteorological data) will be used for evaluation of hail, flooding and drought. Combined with
the crop data (e.g. potatoes die after a few days of flooding, other crops can survive) the
actual damage can be determined. The risk assessment data can also be used for services for
farmers to benchmark their risk and evaluate the business case for prevention. Privacy of
results needs to be discussed with users. Guarantee of confidentiality through a written
agreement at the very beginning of the pilot phase.
13.3 Pilot modelling with ArchiMate The current section presents the "Agriculture C1.2 Insurance Netherlands modelling with
ArchiMate" view point described using the ArchiMate standard. It lists the views and
nomenclatures composing the view point.
13.3.1 Agriculture pilot C1.2 Motivation view
This section provides the "Agriculture C1.2 Motivation view" view defined in the "Agriculture
C1.2 Insurance Netherlands modelling with ArchiMate" view point.
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Figure 46: Agriculture pilot C1.2 Motivation view
Motivation elements are used to model the motivations, or reasons, that guide the design or
change of an Enterprise Architecture. It is essential to understand the factors, often referred
to as drivers, which influence other motivation elements. They can originate from either
inside or outside the enterprise. Internal drivers, also called concerns, are associated with
stakeholders, which can be some individual human being or some group of human beings,
such as a project team, enterprise, or society. Examples of such internal drivers are customer
satisfaction, compliance to legislation, or profitability.
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13.3.2 Agriculture pilot C1.2 Strategy view
This section provides the "Agriculture C1.2 Strategy view" view defined in the "Agriculture
C1.2 Insurance Netherlands modelling with ArchiMate" view point.
Figure 47: Agriculture pilot C1.2 Strategy view
The immediate decision support system is built on top of a data collection and distribution
system. The data collection and distribution system is used to collect sensor data from the
on-board systems and makes them available in a single system. The data distribution system
ensures that the decision support system only interface with a single system, instead of
multiple sensors. The decision support system presents the data from the data distribution
system and collects them in an internal storage system for presentation of current
performance vs. historic performance.
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13.4 Pilot Evaluation Plan
13.4.1 High level goals and KPI's
Two preliminary KPIs that has been identified so far:
● Information correctness: risk assessment success ratio and damage assessment success ratio having an acceptable error rate when tested on historic data that it was not trained on.
● System usage: Number of users of the services, and number of users visiting the website with information about.
13.4.2 Initial roadmap
Figure 48: Agriculture pilot C1.2 initial roadmap
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13.5 Big data assets
Figure 49: Agriculture pilot C1.2 BDVA reference model
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Pilot 12 [C2.1] CAP Support 14.1 Pilot overview
14.1.1 Pilot introduction
In the framework of EU Common Agriculture Policy (CAP), farmers can have access to
subsidies from the EU, that are provided through Paying Agencies or Authorized SMEs (the
Greek case) operating at National or Regional level according to the Member State. For the
provision of the subsidies, Paying Agencies must operate several controls over farmer parcels
in order to verify the compliance of the cultivation with EU regulations. These controls are
carried out with a large support from remote sensing. Today, due to the cost of remote
sensing data, the controls are limited to a sample of the whole amount of farmers
declarations, and the control is often focused on a specific time window, not covering the
whole lifecycle of the agriculture parcel during the year. With the availability of the Sentinel
satellite data, it is now possible to have access to a large amount of free of charge satellite
images providing frequent coverage of the whole EU with a resolution compliant with the
average agriculture field size for many farmers. Therefore, it is possible, through the
processing of satellite time series, to provide services in support to the Paying Agencies and
the authorized collection offices for a more accurate and complete control of the farmers’
declaration. Foreseen services will allow a more complete and efficient management of EU
subsidies, strongly enhancing their procedure for combating frauds or not compliant
behaviours, thus guaranteeing an evident economic return also in terms of saving for ground
surveys and optimization of control management.
14.1.2 Pilot overview
Reference service situation and user needs
Member States must take the necessary measures to ensure that transactions financed by
the EAGF (European Agricultural Guarantee Fund) are implemented correctly. Furthermore,
Member States, through the National Paying Agencies, must prevent irregularities and take
the appropriate actions if they do occur. For this purpose, the national authorities are
required to operate an Integrated Administration and Control System (IACS) in order to
ensure that payments are made correctly, irregularities are prevented, revealed by controls,
followed up and amounts unduly paid are recovered. Controls are currently operated on the
basis of workflows relying on the use of EO data (aerial data, VHR and HR satellite data)
integrated with additional geodata and databases. High and very-high resolution satellite
imagery (HR and VHR) are currently used to check farmer’s declarations, and increasingly to
verify the compliance of their farming practices with agro-environmental rules. In general,
National Paying Agencies operate the workflows through service integrators, that process EO
data and tune the operational workflow. Service integrators are often local enterprises, that
operate with different software solutions based on a common EO data feed. Main
stakeholders, external to the National Paying Agencies and involved in the process, are:
satellite image providers, aerial image providers, value adders on image data, service
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integrators. The value of the market for satellite image providers is in the order of 6-7 Milion
euros per year, with a strong improvement foreseen for 2015.
For its controls, the National Paying Agencies adopt a national agriculture-oriented land cover
reference map (updated in general on a three-years basis with aerial data), and performs
detailed and tailored checks by means of satellite data over risk based and random sample
zones covering 5% of the farms on a yearly basis.
The controls are aimed at:
• To check the presence of cultivated land and specific crops in case of coupling
• To check the diversification of crops according to greening criteria
• To check the presence and maintenance of permanent pastures
• To check the presence and maintenance of land lying fallow
All these controls are operated only on the 5% of the farms with fresh VHR and HR EO data.
On all the other farms, the control must rely on EO data that could have been collected one
or two years before (due to LPIS updating cycle), with a negative impact on farm compliance
evaluation. In addition to this situation, it must be noticed that most of the checks could
provide more reliable results if based on multi-temporal time series, since adopted data could
introduce bias in the interpretation.
Objective
The objective of the pilot is the provision of products and services, based on specialized highly
automated processors processing big data, in support to the CAP and relying on multi-
temporal series of free and open EO data, with focus on Copernicus Sentinel 2 data. Products
and services will be tuned in order to fulfil requirements from the 2015-20 EU CAP policy, and
will be general information layers and indicators on EU territory with different level of
aggregation and detail up to farm level. Therefore, the services, to be tuned and confirmed
with end users, will:
• Identify parcels (monitoring objects) over which the declared crop is potentially different from the one that extracted from the EO models (outliers). The service is based on Sentinel data and machine learning methods for the description of the crop and analytics methods for the identification of the outliers. The service will allow the performing of big data analytics to various crop indicators on parcel level.
• Identify different crops present inside a single farm when the global size of declared surface is exceeding a specific threshold. This is due to the fact that CAP requires crops diversification such that farmers should cultivate at least two/three different crops. The service will be based on the management of optical satellite data together with farmer declarations information and limited ground measures if any, and will provide an indication of possible compliance/not compliance of the farmer vs. EU CAP requirements.
Table 31: Summary of pilot C2.1 (ISO JTC1 WG9 use case template)
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Use case title
CAP Support
Vertical (area) Agriculture
Author/company/email Coordinator: Antonella Catucci/e-GEOS/[email protected]
Actors/stakeholders and their roles and
responsibilities
Satellite Service Providers / Added value maps and products;
Technological and Agricultural Service Company/ providing validation data and information;
End Users/ definition of requirements/provision of crop data/ validation of the service.
Goals The objective of proposed pilot is to provide services in support to the National and Local Paying Agencies and the authorized collection offices for a more accurate and complete control of the farmers’ declaration related to the obligation introduces by the current Common Agriculture Policy.
Use case description In the framework of EU Common Agriculture Policy (CAP), farmers
can have access to subsidies from the EU, that are provided
through Paying Agencies or Authorized SMEs (the Greek case)
operating at National or Regional level according to the Member
State. For the provision of the subsidies, Paying Agencies must
operate several controls over farmer parcels in order to verify the
compliance of the cultivation with EU regulations.
Current
solutions
Compute(System) Different agriculture declaration
management systems are today
available at National or Regional level.
Today, due to the cost of remote
sensing data, the controls are limited
to a sample of the whole amount of
farmers declarations, and the control
is often focused on a specific time
window, not covering the whole
lifecycle of the agriculture parcel
during the year. These controls are
carried out with a large support from
remote sensing.
Storage Local system + web based information systems for general agriculture data management
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Networking Manual assessment of Web, internal data about insured parcels
Software Multiple individual systems, not integrated processing and display
Big data characteristics
Data source (distributed/centralized)
Combination of both types:
Centralized: related to parcel information and provided by the users. The system of the user is a legacy centralized system.
Distributed: satellite optical and SAR data are processed in separate processing platform and then stored in a big-data store and delivered using web services to the user legacy system.
Volume (size) HR Optical S2 data: each tile ~100x100km2 and ~0.5GB. 1 single tile cover each Pilot areas.
Landsat 8: each tile ~185x170km2 and ~0.8GB
SAR S1 data: each Burst ~20x20km2 and ~0.25GB
Velocity
(e.g. real time)
Periodic update (see later on variability)
Variety
(multiple datasets, mashup)
Multiple Datasets: the idea is to include satellite optical and SAR data together with parcel data provided by the users.
Variability (rate of change)
Rate of change depends very much on data source/type:
- Satellite S1/S2: 5 days - Landsat 8: 14 days - Parcels: yearly update
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Time-series analysis must contain a certain number of real measurement in order to give hints on the potential inconsistencies.
Visualization Visualization processed images coupled with data analytics providing information about the current status of the parcel with expected status (compliant with farmer declaration)
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able to detect potential inconsistencies.
Need of plotting and visualization of processing results (multi modal dashboard).
Data quality (syntax) Collected information must be linked to parcels database (can be different across different countries).
Data types Need to analyse multi-dimension (explore) and multisource data.
Data analytics Aggregation mechanism (Drill Down at parcel level (polygon) and station level (point).
Plotting and visualization of processing results (multi modal dashboard).
Big data specific challenges (Gaps)
There is a need for correlating remote sensed crop status and expected behaviour for the crop typology communicated by the farmer.
Big data specific challenges in bioeconomy
The possibility to provide support to the National and Local paying agencies will allow the optimization of economic resources dedicated from the EC to the agriculture domain. These resources could be allocated to other activities in support to the agriculture activities development.
Security and privacy
technical considerations
Reference data about potential frauds will be used. This require a clause on confidentiality and a software component handling security and privacy of the pilot results.
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Verification of the compliance of the cultivation with EU regulations.
More information (URLs)
www.databio.eu
<other URLs to be added later if relevant>
Note: <additional comments>
14.2 Pilot case definition The proposed pilot project has been tailored on the specific needs of three end users, one
operating at National level (Romania Agriculture Ministry), one operating at Regional level
(AVEPA Paying Agency) in one of the most important agricultural regions in Italy and one
operating in Spain. The services that will be provided in the pilot project will rely on the
processing of big amount of data such those provided by Copernicus Sentinel-1 and Sentinel-
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2 satellite, collecting SAR and multispectral image data with a 10-days frequency (that will be
increased to 5-days with the full constellation Sentinel-2A Sentinel-2B Sentinel 1B will be fully
available). Data will also be integrated by time series of Landsat8 satellite, providing images
with a 16-days frequency but at lower resolution.
Geographical location of the pilot:
Table 32: Agriculture pilot C2.1 overview of pilot activities
Romania Italy
Location: South-East Romania
Area size: 10.000 km2
Targeted Crops: 3-10 crop types (Wheat,
corn, sun-flower) TBC
Location: North Italy (Veneto)
Area size: 50.000ha
Targeted Crops: two open crop types
TBC
End-User: National/regional Agencies in charge for the agriculture controls in order to verify
the compliance of the cultivation receiving subsidies with EU regulations. In particular:
Italy: AVEPA is the regional entity in charge for the regional payments in agriculture of
European funds (including FEAGA and FEASR funds) in the Veneto Region. The AVEPA agency
also acquired more responsibility from the Regional Administration to manage the entire set
of international and national funds in the agriculture domain including the CAP
implementation and the payment of the public insurances in case of weather related critical
events. Veneto Region is one of the most important region in the Agriculture and Food
domain in Italy and also the most proactive in terms of international activity related with the
innovation (semi-finalist for the European Communication award PAC 2014).
Romania: APIA (National Subsidy Agency for Agriculture, Ministry of Agriculture) holds
responsibility in Romania of the implementation of CAP mechanisms for direct payments. The
entire procedure is handled by the Integrated System of Administration and Control (IACS)
that also deals with the verification of the compliance of the declarations submitted by the
farmers. Currently, a minimum of 5% from the applications is crossed-checked either by field
sampling or by remote sensing.
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14.2.1 Stakeholder and user stories
Table 33: Agriculture pilot C2.1 stakeholders and user stories
Who (type of
user)
I want to (can you perform some
task)
Why (achieve some goals)
1 – National /
Regional
Subsidy Agency
for Agriculture
I want to verify that farmers fulfil
the CAP criteria related to crop
presence and diversification.
In order verify farmers that receive
simple or decoupled green
payment/subsidies per ha from
European Commission.
2 – National /
Regional
Subsidy Agency
for Agriculture
I want to verify that farmers fulfil
the criteria related to the
maintenance of permanent
pastures.
In order verify farmers that receive
decoupled green
payment/subsidies per ha from
European Commission.
14.2.2 Motivation and strategy
The objective of the pilot is the provision of products and services, based on specialized highly
automated processors processing big data, in support to the CAP and relying on multi-
temporal series of free and open EO data, with focus on Copernicus Sentinel data. In
particular, the output of the project will support the National CAP in the verification of the
compliance of farmer declarations and the automatic identification of possible frauds; this
information could be used by the Agencies also to better plan focused controls. The general
methodology will be based on the comparison of the real crop behaviour (detected by
Remote sensing techniques) with the expected trends for each crop typology.
14.3 Pilot modelling with ArchiMate The current section presents the "Agriculture C2.1 CAP support Italy Romania modelling with
ArchiMate" view point described using the ArchiMate standard. It lists the views and
nomenclatures composing the view point.
14.3.1 Agriculture pilot C2.1 Motivation view
This section provides the "Agriculture C2.1 Motivation view" view defined in the "Agriculture
C2.1 CAP support Italy Romania modelling with ArchiMate" view point.
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Figure 50: Agriculture pilot C2.1 Motivation view
Motivation elements are used to model the motivations, or reasons, that guide the design or
change of an Enterprise Architecture. It is essential to understand the factors, often referred
to as drivers, which influence other motivation elements. They can originate from either
inside or outside the enterprise. Internal drivers, also called concerns, are associated with
stakeholders, which can be some individual human being or some group of human beings,
such as a project team, enterprise, or society. Examples of such internal drivers are customer
satisfaction, compliance to legislation, or profitability.
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14.3.2 Agriculture pilot C2.1 Strategy view
This section provides the "Agriculture C2.1 Strategy view" view defined in the "Agriculture
C2.1 CAP support Italy Romania modelling with ArchiMate" view point.
Figure 51: Agriculture pilot C2.1 Strategy view
The immediate decision support system is built on top of a data collection and distribution
system. The data collection and distribution system is used to collect sensor data from the
on-board systems and makes them available in a single system. The data distribution system
ensures that the decision support system only interface with a single system, instead of
multiple sensors. The decision support system presents the data from the data distribution
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system and collects them in an internal storage system for presentation of current
performance vs. historic performance.
14.4 Pilot Evaluation Plan
14.4.1 High level goals and KPI's
Two preliminary KPIs that has been identified so far:
• Information correctness: inconsistencies success ratio having an acceptable error rate when tested on historic data
• System usage: Number of users of the services, and number of users visiting the website with information about
• Satellite on-the-spot checks %: percentage of agricultural parcels covered with satellite on-the-spot checks. This can “significantly increase the efficiency of on-the-spot checks necessary for CAP payments” (https://ec.europa.eu/agriculture/newsroom/286_en)
14.4.2 Initial roadmap
Figure 52: Agriculture pilot C2.1 initial roadmap
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14.5 Big data assets
Figure 53: Agriculture pilot C2.1 BVDA reference model
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Pilot 13 [C.2.2] CAP Support (Greece)
15.1.1 Pilot introduction
With an annual budget of roughly €59 billion, that accounts for 38% of the EU-28 budget, the
Common Agricultural Policy (CAP) aims to strengthen the competitiveness and sustainability
of agriculture in Europe by financing a range of support measures [REF-10]. CAP effectiveness
is crucial for at least 22 million farmers and agricultural workers but is limited by
administrative burdens, complexity and high implementation costs. The identification of best
practices that lead to the reduction of delivery costs without affecting the effectiveness is a
key priority not only for the National Payment Agencies but also for the EU Parliament. Earth
Observation (EO) has been frequently suggested as the best possible tool for the effective and
efficient implementation of the CAP. However, until now, EO has been limited to performing
“Controls with Remote Sensing” (CwRS) for the purposes of the annual verification of
subsidies claims. This “business process” is applied only to the 5-7% of EU farms and has a
total estimated cost of approximately €40 million per year, which is insignificant when
compared to the €2654.6 million per year of the official annual CAP delivery cost[REF-11]. The
EC and the JRC [REF-12], have together stressed the need for EO-based agricultural
monitoring able to support not only the assessment checks but in total the CAP
implementation and its instruments such as the Good agricultural and Environmental
Monitoring (GAEC) or the Farm Advisory System (FAS). Recent technological improvements in
terms of big data handling, available computing power and the Copernicus Sentinel data and
imagery allows the continuous and automated provision of agri-environmental information’s
for objects being monitored such as the agricultural parcels. Precision and/or Smart Farming
is a sector that relies for many of its key business process on the Earth Observation
technology. It is also a key concept that CAP has promoted as a necessity after 2020 [REF-13],
[REF-14] for the improvement of agricultural production and efficient farm management. In
the upcoming Common Agricultural and Food Policy, which is currently being designed,
Precision and/or Smart Farming and EO are the most valuable tools because their combined
use leads to an optimal and sustainable production and allows the provision of advisory
services based on facts.
15.1.2 Pilot overview
The main scope of this pilot is to evaluate a set of EO-based services designed appropriately
to support key business processes and need of the CAP value chain stakeholders. The pilot
activities will focus on two (2) open crop types (dry beans and peaches) and will be held in
the Northern part of Greece. The services to be tested will rely on innovative tools and
technologies, that will sustain the interconnection with IoT infrastructures and EO platforms,
the collection and ingestion of spatiotemporal data, the multidimensional deep data
exploration and modelling and the provision of meaningful insights, thus, supporting the
simplification and improving the effectiveness of CAP. NP and GAIA Epicheirein will support
the activities of this pilot demonstration.
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Table 34: Agriculture pilot C2.2 overview of pilot activities
Pilot Site
Location North Greece
Area Size 50000ha
Targeted Crops 2 open crop types (dry beans and peaches etc.)
End-Users GAIA Epicheirein
Method
The overall objective of the pilot is to validate a set of EO value-added products and services
designed appropriately to support key business processes and the needs of CAP value chain
stakeholders. The methodology of the pilot activities involves the integration of high power
computing, machine learning methods, geospatial data analytics with data coming from EO
platforms and will allow for integration with IoT based data streams and services. The
convergence of high computing power, machine learning, and satellite imagery is “a perfect
storm that’s just beginning to peak” [REF-15] and as such the ambition of the current pilot is
to exploit the “produced power” for dealing effectively with CAP demands for agricultural
crop type identification, parcel monitoring, collaboration, transparency and analytics. This
way the value chain stakeholders (GAIA Epicheirein, farmers, farming cooperations, etc.) will
benefit from the EO data, supporting the simplification and improving the effectiveness of
CAP.
Relevance to and availability of Big Data and Big Data infrastructure
NP has already started collecting field-sensing data through its network of telemetric IoT
stations, called GAIAtrons. GAIAtrons offer configurable data collection and transmission
rates. Since 01/03/2016 over 1M samples have been collected and stored to NP’s cloud
infrastructure that refer to atmospheric and soil measurements from various agricultural
areas of Greece. Moreover, within the same cloud infrastructure (GAIA cloud), remote sensing
data from the new Sentinel 2 optical products are being extracted and stored since the
beginning of 2016. This comprises both raw and processed (corrected products, extracted
indices) data represented in raster formats that are being handled and distributed using
optimal big data management methodologies.
Benefit of pilot
The pilot activities aim at providing EO-based products and services designed to support key
business processes and needs of the CAP value chain stakeholders, including:
• The farmer decision-making actions during the submission of aid application. More specifically, for each agricultural parcel, the developed services will allow the a) automated validation of the declared crop, b) accurate definition of the eligibility area,
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c) monitoring of the good agricultural and environmental conditions. Each year the Farmer, the beneficiary, must provide evidence to document his/her eligibility. His/her choices during the one-off submission process have great financing impact and may lead to losses or, even worse, trigger penalties. The offered services will boost the farmer’s decision-making power [REF-16] and help them maximize the benefits and minimize the financial risks in relation to the agricultural land for which direct support is requested.
• The Farmer transition towards Smart Farming, i.e. the provision of tools that support not only the compliance with CAP but also assist the adoption and implementation of Smart Farming practices. The proposed services for agricultural and environmental monitoring at parcel level, provide streams of data and facts that can be used for automated irrigation, crop protection, actual crop status and crop variability identification.
15.2 Pilot case definition
Table 35: Summary of pilot C2.2 (ISO JTC1 WG9 use case template)
Use case title CAP Support
Vertical (area) Agriculture
Author/company/email NP, GAIA Epicheirein
Actors/stakeholders and their roles and
responsibilities
GAIA Epicheirein – Supporting role in the farmers’ declaration process
Farmers from the engaged agricultural cooperatives in the pilot area
Goals Crop identification using EO data to check against the declared crop type
Use case description Refer to the pilot case definition section and diagrams in the pilot modelling sections.
Current
solutions
Compute(System) Non-existing system today. The application process relies only on static background maps. The farmer’s decision is not supported by multi-temporal EO data.
Storage -
Networking -
Software -
Big data characteristics
Data source (distributed/centralized)
Centralized (within GAIA Cloud): Field sensing data from GAIAtrons, Remote
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sensing (Earth observation) data, anonymized IASC data
Volume (size) ● ~7.5 TB/year for remote sensing data, including raw data and extracted biophysical and vegetation indices for the pilot areas,
● several GBs/year field sensing data collected by the deployed GAIAtrons (related to the number of GAIAtrons to be used within the pilot activities)
Velocity
(e.g. real time)
Configurable data transmission for field sensing (a new set of measurements is being sent every 10 minutes in present configuration). Every 10 days new EO products available. Within 2018 EO products will be available every 5 days.
Variety
(multiple datasets, mashup)
Field Sensing: Soil temperature, humidity (multi-depth), ambient temperature, humidity, barometric pressure, solar radiation, leaf wetness, rainfall volume, wind speed and direction
Remote Sensing: 13 spectral bands
Variability (rate of change)
Same as above, rate of change depends very much on data source/type.
Big data science (collection, curation,
analysis,
action)
Veracity (Robustness Issues, semantics)
Need for a system that can constantly provide support to the farmers’ declaration process
Visualization Spatio-temporal information visualization for facilitating the declaration process
Data quality (syntax) The quality of field sensing data is being controlled by several filtering, outlier detection and stream processing mechanisms. The integrity of remote sensing data quality is being assessed by a hash check upon product download.
Data types Remote sensing data provided in raster format (.jp2). Field sensing data
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provided as time series unstructured data with configurable frequency
Data analytics Descriptive and diagnostic analytics for CAP support.
Big data specific challenges (Gaps)
There is a need for VHR data for validation and optimization of methodologies. There is a need for smarter fusion of the heterogenous data types that are being collected towards providing accurate insights. To this end, it is important to explore mechanisms that could combine raster and vector data at parcel level (polygon) and station level (point).
Big data specific challenges in bioeconomy
It has been identified that technological trends, such as deep learning techniques, can be exploited for encountering several big data challenges (e.g. data-driven crop classification models, selection of training data sets per crops) and for advancing the effectiveness of traditional machine learning methodologies for crop classification.
Security and privacy
technical considerations
A system intended to collect data from field sensors, installed in remote locations, is definitely going to face network connectivity challenges. In order to provide up-to-date and relevant advices, the system should be able to exhibit high availability and accuracy in its sensor readings and transmission mechanisms. Moreover, field sensing data should be securely transmitted to the cloud infrastructure and protected against various types of attacks that might set the system at risk.
Highlight issues for generalizing this Use
case (e.g. for ref. architecture)
Machine learning methodology for crop modelling
15.2.1 Stakeholder and user stories
Table 36: Agriculture pilot C2.2 stakeholders and user stories
Stakeholders User story Motivation
GAIA Epicheirein
GAIA needs services to support farmers in the declaration processes
Ensure the validity of the declarations and offer a reliable tool/product to the farmers that would strengthen its position as a service provider for the agri-food sector.
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Farmers Farmers need a reliable supporting/consulting tool for the crop type declaration process
Minimize the risk that the process will trigger financial penalties
15.2.2 Motivation and strategy
The main motivation for this pilot is:
• to demonstrate the added-value of EO-based products and services designed appropriately to support key business processes and the needs of CAP value chain stakeholders
• to raise the awareness of the farmers, agronomists, agricultural advisors, farmer cooperatives and organizations (e.g. group of producers) on how new technological tools could facilitate the crop declaration process.
• to increase the technological capacity of the involved partners through a set of pilot activities that involves management of big data for high value crops.
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.
15.3 Pilot modelling with ArchiMate The current section presents the "Agriculture C2.2 CAP support Greece modelling with
ArchiMate" view point described using the ArchiMate standard. It lists the views and
nomenclatures composing the view point.
15.3.1 Agriculture pilot C2.2 Motivation view
This section provides the "Agriculture C2.2 Motivation view" view defined in the "Agriculture
C2.2 CAP support Greece modelling with ArchiMate" view point.
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Figure 54: Agriculture pilot C2.2 Motivation view
Motivation elements are used to model the motivations, or reasons, that guide the design or
change of an Enterprise Architecture. It is essential to understand the factors, often referred
to as drivers, which influence other motivation elements. They can originate from either
inside or outside the enterprise. Internal drivers, also called concerns, are associated with
stakeholders, which can be some individual human being or some group of human beings,
such as a project team, enterprise, or society. Examples of such internal drivers are customer
satisfaction, compliance to legislation, or profitability.
15.3.2 Agriculture pilot C2.2 Strategy view
This section provides the "Agriculture C2.2 Strategy view" view defined in the "Agriculture
C2.2 CAP support Greece modelling with ArchiMate" view point.
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Figure 55: Agriculture pilot C2.2 strategy view
The immediate decision support system is built on top of a data collection and distribution
system. The data collection and distribution system is used to collect sensor data from the
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on-board systems and makes them available in a single system. The data distribution system
ensures that the decision support system only interface with a single system, instead of
multiple sensors. The decision support system presents the data from the data distribution
system and collects them in an internal storage system for presentation of current
performance vs. historic performance.
15.4 Pilot Evaluation Plan
15.4.1 High level goals and KPI's
Two relevant KPIs have been identified so far, namely:
● %Decrease in false crop type declarations following the supporting services vs what
would be expected based on historical data (information correctness measured as
inconsistencies ratio).
● %Accuracy in crop type identification
15.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 (WP4).
Figure 56: Agriculture pilot C2.2 initial roadmap
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15.5 Big data assets The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.
Figure 57: Agriculture pilot C2.2 BDVA reference model
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Conclusion D1.1 provides analysis of agriculture pilots and presents initial pilot description and models.
This description will be used for further development inside WP1, but they are also inputs
important for WP4 and WP5. In initial stage, the document will be used for match making
activities among pilots and technology providers, for modelling and analysis of synergies
among pilots and for definition of DataBio reference architecture.
The work in WP1 will now continue with focus on testing of components and building first
version of pilot applications. After first round of pilots testing of different components from
different producers (technology partners) the pilot experience will be used for definition of
DataBio Reference Big Data Architecture.
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References Reference Name of document (include authors, version, date etc. where applicable)
[REF-01] DataBio website. www.databio.eu. Retrieved 2017-06-20.
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http://pubs.opengroup.org/architecture/ArchiMate3-doc/toc.html
[REF-03] Schellberga J, Hill MJ, Gerhards R et al., 2008. Precision agriculture on
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[REF-04] Segarra E, 2002. Precision agriculture initiative for Texas high plains. Annual
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and World Food Programme, 2015
[REF-10] http://ec.europa.eu/agriculture/cap-overview_en
[REF-11] Bruno CHAUVIN, AGRI.DDG4.J.1, 39th Conference of Directors of EU Paying
Agencies, CAP Delivery costs, AMSTERDAM, 25 - 27 May 2016
[REF-12] Arie van der Greft (DG AGRI), Philippe Loudjani (DG JRC), 2016, “Spotlight on
technology in IACS”, 22nd CAP/IACS conference, 25 November 2016
https://ec.europa.eu/jrc/sites/jrcsh/files/1_loudjani.pdf
[REF-13] Cork 2.0 Declaration 2016. A Better Life in Rural Areas. Prepared by the
participants of the European Conference on Rural Development in Cork,
Ireland, 5-6 September 2016
http://ec.europa.eu/agriculture/events/2016/rural-development/cork-
declaration-2-0_en.pdf
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[REF-14] Louise O. Fresco and Krijn J. Poppe (2016), “Towards a Common Agricultural
and Food Policy”, WUR
http://www.wur.nl/upload_mm/6/b/c/11791580-8cfd-4f29-a8ad-
2d9748c787d0_Towards_CAFP_LR.pdf
[REF-15] Pavel Machalek, co-founder and CEO of Spaceknow
[REF-16] Direct Payments. Eligibility for Direct Payments of the Common Agricultural
Policy, September 2016
http://ec.europa.eu/agriculture/sites/agriculture/files/direct-support/direct-
payments/docs/direct-payments-eligibility-conditions_en.pdf