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Integration of a wireless sensor network and a participatory soil - - PowerPoint PPT Presentation

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Integration of a wireless sensor network and a participatory soil monitoring system for smallholder agriculture PhD proposal qualifier presentation Amsale Zelalem Supervisors: Dr. Javier Morales, Dr.ir. Rolf de By, Dr. Yaregal Assabie

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Integration of a wireless sensor network and a participatory soil monitoring system for smallholder agriculture

PhD proposal qualifier presentation Amsale Zelalem Supervisors: Dr. Javier Morales, Dr.ir. Rolf de By,

  • Dr. Yaregal Assabie

February 26, 2019

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SLIDE 2

Outline

Motivation Methodology: High level sketch Case study Conclusion Execution plan

February 25, 2019 2 / 24

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SLIDE 3

Overview

Motivation Methodology: High level sketch Case study Conclusion Execution plan

February 25, 2019 3 / 24

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High-end agriculture

February 25, 2019 4 / 24

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High-end agriculture

◮ ◮ Digital information and technology support ◮ ◮ Adequate production: quality and quantity ◮ ◮ Nature resilience ◮ ◮ Mechanized and large farms ◮ ◮ Regular follow-up and advises

February 25, 2019 4 / 24

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Low-end agriculture

February 25, 2019 5 / 24

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Low-end agriculture

◮ ◮ Smallholders and subsistence-oriented ◮ ◮ Intuitive farming ◮ ◮ Very low technological base and advisory support ◮ ◮ Deficit production

February 25, 2019 5 / 24

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SLIDE 8

Key drivers: Concept diagram

February 25, 2019 6 / 24

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Gaps

◮ ◮ Extreme scarcity of up-to-date soil data at farm field level ◮ ◮ Inefficient and intuitive use of inputs ◮ ◮ Soil data collection is resource-intensive and difficult ◮ ◮ Advisory systems lack crop-specific soil requirements ◮ ◮ Deficiencies remain undetected until too late

February 25, 2019 7 / 24

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Intervention

◮ ◮ An integrated solution of natural, bio-physical, cultural and technological flavors are needed

February 25, 2019 8 / 24

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Intervention

◮ ◮ An integrated solution of natural, bio-physical, cultural and technological flavors are needed ◮ ◮ Implement a (near) real-time, robust, usable, rapidly deployable and affordable soil data collection and analysis tool at farm field level ◮ ◮ Create a platform for improved information flow from multiple sources to improve agriculture yield predictions and projections

February 25, 2019 8 / 24

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Soil

◮ ◮ A source that supplies plants with water and nutrients ◮ ◮ Every crop needs a special composition of the soil to achieve its potential ◮ ◮ Moisture links drought-climate-vegetation ◮ ◮ Nutrients determine yield quality and quantity

February 25, 2019 9 / 24

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SLIDE 13

Overview

Motivation Methodology: High level sketch Case study Conclusion Execution plan

February 25, 2019 10 / 24

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SLIDE 14

From ground to ground

Sensors continually monitor the soil Network Server Data Sources Knowledge base Application Server February 25, 2019 11 / 24

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SLIDE 15

Methodology: system decomposition

Integration of WSN and participatory soil data acquisition platoform

Usability & impact assessment

Farm identification Input use Crop physiology Crop identification Evaluation

Soil moisture aquisition

Site selection Network design Device calibration & configuration Network deployment Data acquisition Empirical analyis Data validation

Particpatory soil analysis

Recruit volunteers Set science-kit Hub System deployment Data acquisition Evaluation

Agriculture expert system

Pre-processing Integration Knowledge base Inference design Knowldege produce Evaluation

February 25, 2019 12 / 24

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SLIDE 16

Usability and impact assessment: Hypthesis-driven approach

Knowledge produce Empirical & qualitative evaluation Repositories Satellite Crop data Yield data Soil data Input use Imagery Define temporal resolution Define area of interest Atmospheric correction

Land mask Crop type

Biophysical indicators Triangulation

Input Sen2Agri

VIs

Investigation

Experts

Process

February 25, 2019 13 / 24

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SLIDE 17

Soil moisture aquisition: Wireless Sensor Network

Sensing Layer

ECH2O ECH2O

Gateway y CN CNn LoRaWAN Network server TCP/IP MQTT/ Publish Cellular TT TTN Persistent Store TCP/IP-

MQTT/ subscribe

WiFi

Brok roker/to /topic

Application server Application interface Application Layer Backhaul Layer CN CN1

EN EN 1 1 EN EN 2 2 EN EN 3 3 EN EN n n ECH2O 5TM ECH2O 5TM ECH2O 5TM ECH2O 5TM ECH2O 5TM

February 25, 2019 14 / 24

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Participatory soil analysis: Digital citizen science

User-end Back-end

Volunteers’ recruitment & training Kit selection & guideline design Science Hub setup Observation & analysis System design & Implementation

Survey design Pre- processor System interface

Repository Artifact selection February 25, 2019 15 / 24

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SLIDE 19

Agriculture expert system: Artificial Neural Network

Expert System Wisdom/ decision support Land evaluation Yield estimation Input/Data

  • WSN-IoT
  • Participatory soil

analysis

Internal External

  • Experts
  • Literature
  • Satellite
  • Weather stations

External

  • Soil
  • Crop
  • Climate
  • Farm mgt.
  • Topography

Information

External

  • Physical
  • Chemical

External

  • Crop type
  • Crop requirement
  • Yield patterns
  • Weather stations

Crop factors External

  • Farm practices
  • Input use

Social factor External

  • Location
  • Elevation
  • Precipitation
  • Temperature
  • Humidity

Environmental factors Land factors

Knowledge base

Existing data

  • Define rules
  • Set relevance
  • Conceptualize
  • Learn patterns
  • Make

inference

  • Test inference
  • Compute error
  • Back propagation
  • Adjust relevance
  • Commit

* *

Infer respond Learn Facts

* * * *

Organized pre-processed

February 25, 2019 16 / 24

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SLIDE 20

Overview

Motivation Methodology: High level sketch Case study Conclusion Execution plan

February 25, 2019 17 / 24

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Study area: Beshilo basin

! ( ! (

Liwicho Degamote 021 Goro Mender Atnt Mesberiya 030 Kolamote 020 Alanshana Werkaya Kuta Ber Dese

Kutaber Dessie Zuria

517912.118082 517912.118082 527912.118082 527912.118082 537912.118082 537912.118082 547912.118082 547912.118082 557912.118082 557912.118082 567912.118082 567912.118082 577912.118082 577912.118082 587912.118082 587912.118082 597912.118082 597912.118082 1203744.0959161213744.095916 1213744.095916 1223744.095916 1223744.095916 1233744.095916 1233744.095916 1243744.095916 1243744.095916 1253744.095916 1253744.095916 1263744.095916 1263744.095916 Tenta Sayint Simada Mekdela Wadla Kutaber Amba Sel Dawunt Delanta Tach Gayint Meket Dessie Zuria Lay Gayint Legambo Guba Lafto Esite Gidan Dessie Were Ilu Source:EthioGIS2 Bounary Rivers

± ± ± ±

1:2,052,626

1:1,893,707 Dega Kolla Weina Dega Wurch

1:555,769 Kebeles Sites ! ( Town

A B C D E

1:23,173,505

1,202 - 1,700 1,700 - 2,200 2,200 - 2,700 2,700 - 3,200 3,200 - 3,700 >3700

± ◮ ◮ Two districts: Kutaber and Dessie

Zuriya ◮ ◮ Total area of 2206 km2 ◮ ◮ More than 300,000 population ◮ ◮ Rurally dominated and agriculture-dependent livelihood ◮ ◮ Severely damaged soil and high prevalence of food insecurity

February 25, 2019 18 / 24

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Collaborations

February 25, 2019 19 / 24

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Collaborations

◮ ◮ Research collaborations

Application of digital image processing for parcel delineation using remote sensing and GIS Design and implementation of standards for heterogeneous data acquisition, integrations and retrieval

February 25, 2019 19 / 24

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Collaborations

◮ ◮ Research collaborations

Application of digital image processing for parcel delineation using remote sensing and GIS Design and implementation of standards for heterogeneous data acquisition, integrations and retrieval

◮ ◮ Logistics collaborations

Equipment are offered by Dr. Rogier from WRS Network back-end configuration & maintenance assist from Mr. Joreon:LISA (ITO) Grant proposal to USAID: ” Building civic participation, good governance, and resilient communities”

February 25, 2019 19 / 24

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Collaborations

◮ ◮ Research collaborations

Application of digital image processing for parcel delineation using remote sensing and GIS Design and implementation of standards for heterogeneous data acquisition, integrations and retrieval

◮ ◮ Logistics collaborations

Equipment are offered by Dr. Rogier from WRS Network back-end configuration & maintenance assist from Mr. Joreon:LISA (ITO) Grant proposal to USAID: ” Building civic participation, good governance, and resilient communities”

◮ ◮ Possible coherence with parallel PhD works of the EENSAT project

February 25, 2019 19 / 24

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Overview

Motivation Methodology: High level sketch Case study Conclusion Execution plan

February 25, 2019 20 / 24

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Work significance and contribution

February 25, 2019 21 / 24

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Work significance and contribution

◮ ◮ Real-time, affordable, and participatory field-level soil data acquisition system ◮ ◮ Integration of multiple data sources to complement agricultural decisions ◮ ◮ Produce baseline for satellite data calibrations & validations ◮ ◮ Fill farm-level information gaps to precision farmings

February 25, 2019 21 / 24

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Work significance and contribution

◮ ◮ Real-time, affordable, and participatory field-level soil data acquisition system ◮ ◮ Integration of multiple data sources to complement agricultural decisions ◮ ◮ Produce baseline for satellite data calibrations & validations ◮ ◮ Fill farm-level information gaps to precision farmings

February 25, 2019 21 / 24

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Overview

Motivation Methodology: High level sketch Case study Conclusion Execution plan

February 25, 2019 22 / 24

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Way forward and possible outcomes

March 2019-Feb. 2020

  • Field visit & site selection
  • Network deployment
  • As-Is assessment

March-Sep. 2020

  • Preliminary

analysis

  • Phase I deliverable
  • Oct. 2020-Sep. 2021
  • Participatory data

collection

  • Date pre-process

Oc Oct.

  • t. 2021-July 2022
  • Mod

Modeling & & analys ysis s

  • Final delive

verab rable Farm-level soil data acquistion:

  • pportunities &

challenges

  • A WSN design &

implementation to support sustainable farming practices

  • Empirical evaluation and

performance assessment of WSN in remote out-door setup

  • Digital citizen science

for continous soil monitoring : the case for smallholder farmers

  • Use of mobile soil

chemical laboratories: pros & cons

  • Integration of IoT,

citizen science and Remote Sensing (RS): robust and scalable primary data collection tool for agriculture

  • AI for agriculture

decision support

February 25, 2019 23 / 24

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SLIDE 32

Thank You

February 25, 2019 24 / 24