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.

8/5/2019

15

Acknowledgements

29

• SD Governor Research Center Program

• EDA University Center Program

• Libyan Scholarship program

• EE & CS department

• Dr. Gary Hatfield

• Dr. Emmanuel Byamukama

• Jason Sternhagen

Dr. ByamukamaDr. Hatfield Mr. Sternhagen