Multisensory Agricultural Monitoring Platform

PI: Ketan Rajawat (EE)

Co-PI: Rajesh Hegde (EE)

Multi modal PBX/Voice server

Database Server (PC Debian GNU/Linux)

SMS Gateway

Get Crop Status via SMS and Voice Call

Get required disease infomation and Store Call information

Make a call and follow instructions

Getting crop infestation knowledge data from

central disease server or distributed servers

Web Server (PC Debian GNU/Linux) Distributed

Servers

Goals

• Agricultural practice in India is characterized by a large pool of local knowledge

• Communities thrive on local wisdom about dealing with local environment, infestation, etc.

• However: central planners often miss this data

• Bridge the gap between: technical knowledge & longitudinal farm experience

• Goal: agricultural monitoring, data collection, presentation, trend mapping, visualization

Objective

• Multi-sensor agricultural monitoring and information dissemination platform

ZigBee/WiFi gateway

ZigBee/GPRS

gateway

Sensor nodes

Cell phone

network coverage

“Data Mule”

(Farmer)

WiFi communication range

Data Mule’s

movement path

Deliverables

• Identification and deployment of affordableand usable sensors

• Architecture: mobile data collection agent

• Backend database + data analytics codebase

• Web portal with maps & alert services

Motivation

• Enable epidemiological studies at central level

• Allow data scientists to spot trends

• General early warning signals on movement of infestations and impact of weather conditions

• Overall: enhancing P2G and G2P

Novel Aspects

• Energy & bandwidth efficient data collection1

• Novel strategies for distributed storage2

• Succinct & meaningful data visualization3

• Filtering, data cleansing, & outlier rejection• Path planning• Beyond

– Predictive models for diseases– Real-time query resolution

1. K. Rajawat, A. Cano, and G. B. Giannakis, “Network-compressive coding for wireless sensors with correlated data,” IEEE Transactions on Wireless Communications, vol. 11, no. 12, pp. 4264-4274, Dec. 20122. Lakshmi J Mohan, Udaya Parampalliy, Ketan Rajawat, and Aaron Harwood, “An optimization design framework for repair efficient storage codes in geo-diverse clusters,” submitted to IEEE Globecom 20173. K. Rajawat and S. Kumar, "Stochastic Multidimensional Scaling," IEEE Transactions on Signal and Information Processing over Networks - Special Issue on Distributed Information Processing in Social Networks, vol. 360-375, no. 2, pp. June 2017.

WP1: Sensing technologies

• Investigation & testing of sensing technologies– Agricultural sensors for use in Indian context

– Field testing of low-cost sensors

Air temperature Leaf wetness

Air relative humidity Stomatal conductance

Solar radiation Soil temperature

Wind velocity Soil moisture content

Rainfall Soil pH

Soil salinity Soil bulk density

Soil hydrolic conductivity Cameras

WP2: Architectural issues

• Design of sensor-gateway data collection

– Energy efficient + low power protocols

– Sleep-wake protocols for energy conservation

• Lab testing of appropriate protocols

– Implementation on test bed in lab

• Reliability and stress testing

– Lifetime maximization

– Operation under harsh conditions

WP3: Data Mule

• Algorithm design for low-cost data collection

– Data compression

– Path planning

– Distributed storage

• Implementation of data fetching protocol

– At WSN lab in EE dept.

– Ensure robustness to delays and losses

• Field testing

S. Kumar, R. Jain, and K. Rajawat, "Asynchronous Optimization Over Heterogeneous Networks via Consensus ADMM," IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 1, pp. 114-129, Mar. 2017.

WP4: Backend Design

• Remote database server

– Pulling data from data mule

– Pushing queries to sensors through data mule

• Data analytics

– Storage, filtering, interpolation, prediction, outlier rejection, data cleansing, denoising

• Visualization & Mapping

• Real-time algorithms for query processing

WP5: Web Portal

• Software specifications for web portal

• Multimodal information dissemination tools– Text & map from backend

– Automated audio text messages

• Design & testing

• User-aided testing

• WP6: Integration & field testing

Deliverables• Year 1: Investigation and field

testing of agricultural monitoring sensors

• Year 2: Implementation and testing of various routing, data collection, data analytics, and visualization algorithms

• Year 3: Field testing of the routing and data collection algorithms

• Year 4: Backend ready, allowing collection, storage, and processing of data

• Year 5: Web portal online and ready for deployment

SWP Year 1 Year 2 Year 3 Year 4 Year 5

1.1

1.2

2.1

2.2

2.3

3.1

3.2

3.3

4.1

4.2

4.3

4.4

5.1

5.2

5.3

5.4

6

Specific Technology outcome at Y1

• We expect to have completed the following at the end of the first year:– Comparative analysis of agricultural sensors

available in the market and their usability in the Indian context (field testing)

– Various algorithms for routing and data collection (SWP2.1 and SWP3.1)

Thank You