From Precision Agriculture to Smart

Farming

Corné Kempenaar Capigi conference Connecting the future, April 2, 2014

Content

Introduction to the topic of today

Some precision agriculture results

● Crop, soil and yield monitoring

● Variable rate application at grid level

● Individual plant treatment (the next level)

Conclusions and challenges

Discussion

Our challenge: More with less

Where we are now

Where we want to be

Ecological

Footprint

FoodDemand

What is precision agriculture ?

Precision agriculture (PA) is a farming management concept

based on observing, measuring and responding to variability

in crops / livestock

Spatial and temporal variation

Detection, decision making and implementation

Sensors, big data, DSS, GNSS, implements, ICT, smart phone,

FMS, Apps, Robotics, .........

Precision agriculture according to Univ. Wisconsin

Precision Agriculture 1.0

‘Controlled traffic farming’

Precision agriculture 2.0, the next step

Scales in precision agriculture

Grid

• In practice

Plant

•On station research

Leaf

•On institute research

Disease

•On institute and university research

National satellite data portal

Without: ● Geometric correction ● Mosaicing ● Cloud detection and filtering ● Atmosferic correction ● End products/indices

Sateliet Data Bands/polarisatie Spatiale Resolutie Temporele Resolutie

Formosat Panchromatisch Multispectraal

Black/white Blue, Green, Red, NIR

2 meter 8 meter

Every 10 days

DMC Multispectraal Green, Red, NIR 22 meter Ca. 3x per week

Radarsat Radar HH+HV polarisatie VV+VH polarisatie

25 meter Every 24 days

Beschikbare satellietbeelden binnen NSD vanaf Maart 2012

DMC versus Formosat

DMC (22 m) NDVI 4-Sep-2012

Formosat (2/8 m) NDVI 1-Sep-2012

Noordoostpolder

Geo-databank of an arable crop rotation Farm De Drieslag, Dronten

Satellite images of winter wheat field

50x winter wheat NDVI curves in Flevoland

NDVI groeicurve is een unieke kwantificering van de gewasontwikkeling per perceel

Ism teeltadviseurs verdere analyse van groeicurves

● Wat is de optimale groeicurve?

● Wat is de relatie met opbrengst?

Potato production and storage

Biomass sensors

Cropscan

Yara N Sensor (ALS and passive)

Terra (Aster sensor), Worldview-2 and TerraSAR-X satellites

90° sensed

area

v

90°sensed

area

Reglone

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 20 40 60

Reflection parameter CropScan

Min

imu

m e

ffe

cti

ve

do

se

(l/h

a)

Decision support model

90° sensed

area

v

90°sensed

area

Acknowledgement: Yara, Homburg Holland, PPO

Grid treatment: sensor op cabine of tractor, DSS, VRA on the go, as applied map

Reglone

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 20 40 60

Reflection parameter CropScan

Min

imu

m e

ffe

cti

ve

do

se

(l/h

a)

Use VRA in practice: Leipzig, 2009

Yara N-Sensor plus conventional sprayer

MLHD PHK dosing algorithm Reglone, normal, 1x

Ware potatoes BBCH GS 91, 7 august, sunny, hot (30 °C)

Pictures were taken four days after treatment

Fixed rate: 2,5 L Reglone/ha @ 300 L water per

ha

Variabele rate: av. 1,5 L Reglone/ha @ 200 L water

per ha

Figure x. Worldview-2 image, 15-08-2011, Flevoland. Test parcel for variable rate application in yellow outline (above). WDVI image of test parcel (lower-left). Reglone dose instruction map (lower-right)

Grid treatment: based on satellite image,

DSS and task map (2011)

Acknowledgement: TerraSphere, NSO en Mts Zondag

or sensing from UAS,

DSS en task map (2012)

Acknowledgement: TerraSphere, Vd Borne

Formosat image used for potato haulm killing (2013)

Task map potato haulm killing (2013) with Akkerweb

As-applied map Yara N-Sensor

Other precision agriculture applications in

development

Variable seeding/planting density

VRA fertilizer application

Soil pH management

VRA irrigation

Selective harvesting

Nematode management

Disease management

Weed management

......................

wikipedia.org

fieldcopter.eu

Veg. indices versus N-content biomass

Model for Nitrogen application in potato

Screendump of the N-advise model

Fuel use monitoring -> input soil management

(2013)

30

Printscreens

‘making of task

map clay content

-> VRA soil

herbicide weed

control)

Weed detection with near sensing camera’s

c

b

d 1.46 m

0.01 m

a

Conclusions and recommendations

Smart farming requires:

● Accurate data soil, climate, crop and livestock conditions

● Data infrastructure to analyse and apply the (big) data

● DSS that translate spatial and temporal variation in actions

● Implements for site or animal specific actions

● Integration of technology at farm and chain level

● Bottom up development

Cooperation between suppliers and end-users

Yield potential map concept will boost PA

Autonomous platforms in crops will change level of

precision in PA

Towards smart farming tech. (P.A. 2.0)