Spatial Analysis For Generating

Recommendations For

Agricultural Crop Production

Yash Jain, Amita Sharma, Sanjay Chaudhary,

Vikas Kumar Tyagi

Dhirubhai Ambani Institute of Information And

Communication Technology

Presented At

ICGTA 2012

IIT Bombay

Outline

Introduction

Motivation

Problem Statement

Methodology

Spatial Data

Analysis

Generating Recommendations

Ontologies and SWRL

Current Work

Future Work Plan

Introduction

Wide uncertainty in weather pattern all over the country.

Crop production has been affected by the changing

climatic conditions.

There is a need to monitor and foresee the agricultural

production based on the changing climate.

Moreover there is a need to generate recommendations

based on the climate and soil conditions.

We want to generate recommendations for cotton crop in

the North Gujarat region.

Motivation

Cotton – an important non-food crop provides lint to

textile industry, as high protein feed to livestock, oil for

human consumption, byproducts used as fertilizer,

produce paper, cardboard, etc. [1]

India is the 2nd largest cotton producer and consumer

[2]

Gujarat is second in cotton production after

Maharashtra.

North Gujarat contributes maximum in cotton

production in Gujarat.

Contd..

Different climate variable affect cotton production in

different ways and by different amount

We need to analyze the weather parameters with respect

to cotton yield to get impact of these parameters on

cotton yield or cotton production

Other variables affecting cotton are soil moisture,

relative humidity, bright sunshine hours etc.

Recommendations/ Alerts can be generated by analyzing the weather parameters and shifts in climate.

Rainfall Variations in Banaskantha, 1991-

2008

Yield Variations in Banaskantha, 1991-2008

Methodology

Obtain spatial data related to climatic parameters as well

as past data related to agriculture and integrate the data

obtained from different sources.

Develop algorithm for generation of alerts/

recommendations based on spatial data processing and

knowledge base.

Define interfaces to distribute the information to the end

users.

Proposed Architecture of the System

[2][

5]

Experiments and Results

S

No

Variable Standard

Error

Coefficient t-

Statistics

p-

value

R-

Square

F-test

1 Intercept 173.25 811.73 4.69 0.001

87.5

0.0003

2 RF_June 1.43 -3.14 -2.20 0.053

3 RF_July 0.31 -1.72 -5.40 0.000

4 RF_Aug 0.31 0.83 2.67 0.024

5 RF_Sep 0.60 1.36 2.28 0.046

6 RF_Oct 1.27 -4.03 -3.18 0.010

Creation of Spatial Database

GIS is playing an increasing role in agriculture

production throughout the world by helping farmers

increase production, reduce costs, and manage their land

more efficiently[2].

Combination of ES and GIS can make the geography

information to bring into the ES decision process[3].

An attribute database is created for the districts of state

which stores the data regarding the meteorological

parameters and soil condition.

The climatic parameters such as rainfall, surface

temperature, humidity and soil qualities such as soil

nutrient contents will be stored in the attribute database..

Analysis of the Spatial Data

The attribute database shall be queried to find out areas

with some threshold parameters.

We shall execute spatial queries to identify specific

scenarios. For example:

◦ Identify areas where rainfall is less:

◦ Identify areas where irrigation facilities are available.

Spatial overlay analysis will be done to identify areas

with typical combinations of temperature, rainfall and

soil quality.

Analysis Of Spatial Data(Contd..)

The growth patterns of cotton in North Gujarat can be

analyzed by performing an overlay analysis of the crop

yield along with meteorological parameters and soil

data.

The overlay analysis reveals relationship of crop yield

with meteorological parameters. Moreover it also helps

to classify data based on climatic parameters and soil

conditions.

The classified data will be passed to the reasoning

engine for generation of recommendations.

Ontologies In the context of computer and information sciences, an

ontology defines a set of representational primitives with

which to model a domain of knowledge or discourse.

The representational primitives are typically classes (or

sets), attributes (or properties), and relationships (or

relations among class members).

We have developed a cotton ontology by extending

AGROVOC.

Ontologies provide a conceptual knowledge base for the

application.

An ontology for rice has been developed for helping the

researchers retrieve documents with greater precision.[4]

Semantic Annotations can be added using domain

ontologies[5].

Source: http://keet.wordpress.com/2009/11/20/72010-semwebtech-lectures-34-ontology-engineering-top-down-

and-bottom-up/

Semantic Web Rule Language

SWRL allows reasoning about OWL individuals. As

defined by W3C, SWRL extends the set of OWL axioms

with Horn-like rules.

It combines the sublanguages of OWL-DL, OWL Lite

and RuleML. Rules are of the form of an implication

between an antecedent (body) and consequent (head).

For example:

Cotton(?x1) ^ hasSoilTemperature(?x1,?x2) ^

swrlb:greaterThan(?x2, 25) ^ hasHumidity(?x1,?x3)

^ swrlb:greaterThan(?x3, 80) ->

hasPossibilityOfOccurence(?x1, RootRot)

Generation Of Alerts/Recommendations

The processing of spatial data based on spatio temporal

modeling will provide useful information regarding the

possible crop outcome in a particular region.

Based on the outcomes, alerts/recommendations can be

generated for a specific region depending on its climatic

conditions.

The recommendations will guide the farmers in their

choice for fertilizers, crop and pesticides.

Interfacing Rule Engine With GIS

We have to interface the reasoning engine with the GIS

server.

We are currently exploring the ways of accessing

ArcObjects with Java API.

An interconnection component will be developed which

will serve to transfer the data obtained from GIS

analysis to the rule engine in an XML format.

Finally a service oriented architecture will be developed

that will allow access to our services in a platform

independent manner.

Information Distribution To End Users

We need to define interfaces through which end users

can request for services and receive the required

information.

One possible way is to have a web based access to the

information. The web services will be exposed and can

be invoked by a web application which takes as input

the location of the user and provides alerts and

recommendations

Information Distribution through SMS.

Current Work

Gathering Spatial Data from multiple sources.

Working on a GIS software to add data and perform

analysis.

Building an attribute database for the districts of Gujarat

which comprise of meteorological data as well as soil

parameters.

Adding concepts and rules to the Ontology.

Identifying ways to interface GIS and rule engine.

Future Work

Integrating spatial data available for different climate

parameters.

We shall use LISS3 as well as AWiFS data for

identification of areas growing cotton.

Developing algorithms for processing of spatial data and

generating alerts.

Defining interfaces to distribute information.

Improving Cotton Ontology for by adding new concepts

and rules targeted towards specific cotton varieties.

Developing a Service Oriented Architecture to expose

the functionality as Web Services.

Acknowledgement

This work is a part of on-going R&D project “Title of

the Project: "Service-Oriented Architecture for Spatial

Data Integration and Spatial Reasoning" funded by

NRDMS, Department of Science and Technology,

Government of India.

We would like to acknowledge Krishi bhavan,

Gandhinagar and Krishi Vigyan Kendra for their support

in terms of meteorological data as well as data related to

cotton crop.

References

[1]Freeland Jr., Thomas B. , Pettigrew, Bill, Thaxton, Peggy, Andrew,

Gordon L., “Agrometeorology and Cotton Production” Guide of

Agricultural Meteorological Practices (GAMP), 2010, edition

(WMO-No.134), Chapter 13.

[2] Osakwe, Emeka, “Cotton Fact Sheet India”, International Cotton

Advisory Committee, May 19, 2009.

[3] Zhiqing Zhu, Rongmei Zhang, and Jieli Sun. “Research on gis-

based agriculture expert system.” In Proceedings of the 2009 WRI

World Congress on Software Engineering – Volume 03, WCSE ’09,

pages 252–255, Washington, DC, USA, 2009. IEEE Computer

Society

[4] Aree Thunkijjanukij,” Ontology Development For Agricultural

Research Knowledge Management: A Case Study For Thai Rice”,

Phd Dissertation, Kasetsart University, 2009.

Contd..

[5] N. Agarwal, M. Rao, S.S. Mantha, and J.A. Gokhale. Annotation of

geospatial data based on semantics for agriculture: Case study for

india. In Computer Research and Development (ICCRD), 2011 3rd

International Conference on, volume 1, pages 139 –142, march

2011.

[6] M. Shoaib and A. Basharat. Semantic web based integrated

agriculture information framework. In Computer Research and

Development, 2010 Second International Conference on, pages

285 –289, may 2010.