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8/5/2019
1
Agriculture Sensors, Probes And Their Integration Into Weather-soil Station For Real-time Field Monitoring In Precision
Agriculture
Ahmed El-magrous & Qiquan (Quinn) Qiao*
Graduate Coordinator Of Electrical EngineeringDirector For EDA University Center For Commercialization Of Sustainable Energies
And Precision Agriculture Sensors TechnologiesNSF IUCRC Planning Grant Site Director
Department Of Electrical Engineering & Computer ScienceSouth Dakota State University
Brookings, SD
Innovations In Ag Series Webinar
North Central Regional Center For Rural Development (NCRCRD)
7/30/2019
1
Outline
• Introduction
• Problems
• Motivation and objectives
• Agricultural Phosphate & Related Sensors
• Sensors networks
• Conclusions
• Future work
• Acknowledgement
2
8/5/2019
2
Introduction• Precision Agriculture (PA)
PA is a farm management concept that relies on data and data analysis to perform; • Support farmer’s decision-making process [1].
• Manage and control the primary agricultural factors such soil fertility, soil quality, water, weeds, pests, diseases, biodiversity.
• Manual collection of data results in variations due to the higher likelihood of incorrect and imprecise measurements taken from the field
3[1] T. Rowlandson, M. Gleason, P. Sentelhas, T. Gillespie, C. Thomas, and B. Hornbuckle, "Reconsidering leaf wetness duration determination for plant disease
management," Plant disease, vol. 99, no. 3, pp. 310-319, 2015.
Figure 1. Example of PA application System.
Internet of Things (IoT)
• IoT refers to the ability to connect billions of physical devices around the world to the internet for collecting and sharing data [1].
• IoT cloud platform;
• Platform and infrastructure is the pillar of any project to gain maximum benefits in terms of security, economics, reliability, and robust performance.
• Many of IoT cloud platforms are available with a difference in services quality and cost; most common IoT provider as following;
• Amazon Web Services (AWS) IoT platform.
• Microsoft Azure IoT HUB.
• IBM Watson IOT platform
• Google cloud platform
4[1] S. Ranger. (2018). What is the IoT? Everything you need to know about the Internet of Things right now. Available: https://www.zdnet.com/article/what-is-the-internet-of-
things-everything-you-need-to-know-about-the-iot-right-now/
Figure 2. Example of improve safety with connected cars.
AWS IOT Platform.
8/5/2019
3
Wireless Sensor Networks (WSN)
• Wireless sensor networks interface
• Three different architectures for deploying WSNs in the field are fixed architecture, mobile architecture, and hybrid architecture[1].
• Most common used architecture is fixed architecture.
• Network nodes are vary based on its role in WSN application
• Sensor node.
• Base node.
• Super node.
5
[1] G. Deepika and P. Rajapirian, "Wireless sensor network in precision agriculture: a survey," in Emerging Trends in
Engineering, Technology and Science (ICETETS), International Conference on, 2016, pp. 1-4: IEEE.
Internet
Gateway
Cloud
Crop Field Side
Sensor node
Base node
Super node
Figure 3. The wireless sensor networks interface.
Problems
• lack of an inexpensive real-time monitoring tool for weather conditions, soil properties, and crop health.
• lack of field-specific information makes it difficult to make precise decisions and crop management.
• Lack of soil nutrient information such as nitrogen, phosphorous and water limits production of adequate food to feed the rapidly-growing world population.
• The deficiency of the soil nutrients adversely hampers crops’ function and timely growth.
• To overcome this deficiency, producers commonly apply chemical fertilizers and organic materials containing nitrogen, phosphorus, potassium to enhance soil fertility and yield in agricultural production.
6
8/5/2019
4
Motivation
• Design, development, and testing of a customizable and cost-effective Weather-Soil Sensor Station (W-SSS) to collect real time soil properties, climate conditions, and crop information.
7
• Connect fields, growers, and experts in real time for making right decisions based on site-specific information collection.
• Establish the effectiveness of white mold disease forecasting system based on leaf wetness and other variables.
Objectives
Agricultural Phosphate & Related Sensors
Sensor research
8
Funding sources:Department of Commerce EDA University Center ProgramSD Governor Research center program
8/5/2019
5
Screen printed electrode electrochemical phosphate sensors using AgNWs & AMT
• When AgNWs were added into AMT/SPE, a significant increase (five folds) in anodic peak current was observed compared to that without AgNWs.
• Attributed to the faster electron transfer and transport between AMT and the working electrode due to the presence of AgNWs
9
0.00 0.25 0.50 0.75 1.00 1.25-200
-150
-100
-50
0
50
100
150
200
Cu
rren
t (
A)
Potential (V)
1 mM 750 M
500 M 250 M
100 M 50 M
10 M without ions
Green points are taken as anodic peak current
0.00 0.25 0.50 0.75 1.00 1.25-400
-200
0
200
400
600
Cu
rren
t (
A)
Potential (V)
1 mM 750 M
500 M 250 M
100 M 50 M
10 M Without ions
Green points are taken as anodic peak
current
AMT/SPE
AMT/AgNWs
/SPE
IEEE Sensors Journal, 18 (9),
3480-3485, 2018
)1......(36751127 2
3
4012
6
24743 OHOPMoHOMoPOH
)3......()1069.2( *
0
2/12/1
0
2/35 CVADnip
Funded by EDA University Center and SD
Governor Research center program
Phosphate sensor linearity
• AgNWs increased the sensitivity from 0.1 µA/µM to 0.71 µA/µM.
• Detection range = 5 µM - 1 mM.
• Limit of detection (LOD) = 3µM for AMT/AgNWs/SPE.
• Limit of detection (LOD) was calculated using LOD = 3σ/S, where σ and S are standard deviation and sensitivity.
10
0 200 400 600 800 10000
200
400
600
800
AMT/AgNWs/SPE
AMT/SPE
fit data for AMT/SPE
fit data for AMT/AgNWs/SPE
Cu
rren
t (
A)
Concentration (M)
IEEE Sensors Journal, 18 (9),
3480-3485, 2018
Funded by EDA University Center and SD
Governor Research center program
8/5/2019
6
Mercury ions (Hg2+) sensors based on GO-AgNW nano-composites
• Due to the formation of R-COO-Hg2+-COO-R linkage, the sensor showed a strong affinity to Hg2+, while other heavy metal ions had no interference.
• Hg2+ ions added by soaking GO-AgNW nanocomposites into HgCl2 solution followed by applying negative voltage (- 0.4 V for 500 s) to reduce Hg2+ to Hg.
• Square wave voltammetry was carried out subsequently to oxidize (anodic stripping) absorbed mercury Hg to Hg2+.
• Eexcellent repeatability, reproducibility, and applicability for the determination of Hg2+ in tap water.
11ACS Applied Nano Materials, accepted, 2019.
Dip GO-
AgNW into
HgCl2
Ethanol
Square wave anodic stripping voltammetry (SWASV)
• Highly sensitive to Hg2+ in the range of 1 - 70 nM.
• Sensitivity of the sensor was ~ 0.29 µA/nM according to the slope of the linear curve. 12
0.2 0.4 0.6
0
20
40
60
Potential (V) vs. Ag/AgCl
GO
GO-AgNW
(b)
Cu
rren
t (
A)
0.0 0.2 0.4 0.6
0
20
40
60
Cu
rren
t (
)
Potential (V) vs. Ag/AgCl
0 nM
1 nM
10 nM
20 nM
30 nM
50 nM
70 nM
(e)
0 20 40 60 80
40
50
60
70
Pea
k C
urr
ent
(
)
Concentration (nM)
R2= 0.9947(f)
ACS Applied Nano Materials,
accepted, 2019.
Funded by EDA University
Center and SD Governor
Research center program
8/5/2019
7
Selectivity, reproducibility, & repeatability
13ACS Applied Nano Materials, accepted, 2019.
50 nM
Selectivity
Repeatability: The same sensor, different measurements.
Reproducibility: Three different sensors measuring the same concentration.
0 2 4 6 8 10
0
20
40
60
Number of measurement
RSD = 3.01%(b)
Pea
k C
urr
ent
(A
)
0.0 0.2 0.4
0
20
40
60
Cu
rren
t (
)
Potential (V) vs. Ag/AgCl
(c)
1 2 30
20
40
Pea
k C
urr
ent
(
)
Sensor Number
RSD = 2.0%
ACS Applied Nano Materials,
accepted, 2019.
Funded by EDA University
Center and SD Governor
Research center program
Analysis of Real Tap Water Samples
SampleConcentration of Hg2+
Recovery (%)Add (nM) Found (nM)
Tap water 1 1.0 0.9 90.0
Tap water 2 10.0 8.7 87.0
Tap water 3 70.0 69.0 98.6
14
Determination of Hg2+ in real water samples using GO-AgNW nanocomposites modified Pt sensors.
ACS Applied Nano Materials, accepted, 2019.
8/5/2019
8
Wireless sensors networks
15
Sensor Nodes
16
4”8”
16”
Super nodeSensor node
End user
19th Annual IEEE International Conference On Electro Information Technology (eit2019), SDSU, Brookings, SD 57006, May 20-22, 2019.
8/5/2019
9
Wireless sensor networks
17
Gateway
Crop Field
Side
Sensor node
Base node
Super node
Sensor node
Super node
LoRa• Long distance, up to
15Km • Low power consumption,
a coin battery for one year
Funded by EDA University
Center and SD Governor
Research center program
19th Annual IEEE International Conference On Electro Information Technology (eit2019), SDSU, Brookings, SD 57006, May 20-22, 2019.
Weather-soil sensor station
18
• For building robust and accurate model based on trusted data, a weather/soil sensor-
station has been developed by our lab by using most highest-accuracy sensors. The weather/soil sensor-station has 10 different sensors to collect required data from the
field of study in real time.
Raw sensed data transmit out of the station to the database server by using the internet of
things.
To reduce the risk of losing data during transmit process, a copy of data save into SD card
MicrocontrollerSecure
Internet
Getaway
Air temperature
Relative humidity
Wind speed
Wind direction
Rainfall
Leaf wetness
Sun radiation
Soil moisture at surface
Soil temperature at 30cm
Soil moisture at 30cm
Server
Database
Weather/soil sensor-station interface diagram.
Power Supply
A2
DC
on
verte
r
Sensors
8/5/2019
10
19
Temp & RH sensor
Wind speed sensor
Wind direction sensor
Rainfall sensor
Soil temp sensor
Leaf wetness sensor
Soil moisture sensor
SD card Real time clock
Sun radiation sensor
Weather-soil sensor station
LoRa
Power System
20
5-12 Volt
A 55Ah AGM sealed lead acid battery (12 Volt)
A Newpowa 10W 12V polycrystalline
A Morningstar SunGuardcharge controller
The Arduino MEGA
Power system
A RECOM model R-78W-05
DC/DC switching regulator
8/5/2019
11
Four stations prototypesFour weather-soil sensor stations have developed in our lab and deployed at different locations, different soil topography and soil texture, in South Dakota during 2018 Soybean season.
Station #1 is located in Eureka Township , SD
http://www.aelmagrous.com/SensorDemo/retrive.php
Station #2 is located in Volga, SD
Stations #3 & #4 are located in Red Rock, SD
More than 500,000 data point have been collected from each sensor in real-time
Area of study
22
53.2 mi
13.9 mi
0.52 mi
Four different selected locations for stations deployment
8/5/2019
12
Raw data validation
23GUI data validation tool deployment
Transmitted data validation & collection
• W-SSS: Weather-Soil Sensor Station
24
• All measured data are within the physical sensor range.
• Good quality of data.
• Data loss rate of 0.013% and 0.009% for the W-SSS1 and WSSS2
IEEE Internet of Things Journal,
accepted, 2019.
Funded by EDA University
Center and SD Governor
Research center program
8/5/2019
13
Real time data access
25
• A web and mobile based applications for all online target services will be developed to
meet end users requirements. The applications will be the hub for both the knowledge experts and the growers.
It will enable users to have a comprehensive access to system’s resources in a handy and
inexpensive way..
Mobile based application to access sensed data
Real time data access
26
8/5/2019
14
A five-year $725,000 grant from U.S. EDA University Center Program
27
Conclusions
• Phosphorous and heavy metal sensor probes have been developed.
• All four weather/soil sensor-station work perfectly and stable during the whole 2018
soybean season.
• Preliminary tests show this system is effective, practical, and would enable farmers to
make better informed decisions in their operations.
Future work
• Improve nodes communication to the super-node for long renege using a Long Range
Wide Area Network (Lo-RaWAN) Wi-Fi module.
• Build a fully remotely controlled soil-buried sensor nodes.
• Improve the mobile app for inquiring collected data and building field history
• Submit USDA SBIR/STTR projects.