Analysing dynamics of crop problems by applying text analysis methods on farm advisory data of eSaguTM

Authors:
R. Uday Kiran;P. Krishna Reddy;M. Kumara Swamy;G. Syamasundar Reddy
Affiliations:
Media Lab Asia Project, ICTs for Agriculture and Rural Development, International Institute of Information Technology (IIIT-H), Gachibowli, Hyderabad, Andhra Pradesh, India.;Media Lab Asia Project, ICTs for Agriculture and Rural Development, International Institute of Information Technology (IIIT-H), Gachibowli, Hyderabad, Andhra Pradesh, India.;Media Lab Asia Project, ICTs for Agriculture and Rural Development, International Institute of Information Technology (IIIT-H), Gachibowli, Hyderabad, Andhra Pradesh, India.;Media Lab Asia Project, ICTs for Agriculture and Rural Development, International Institute of Information Technology (IIIT-H), Gachibowli, Hyderabad, Andhra Pradesh, India
Venue:
International Journal of Computational Science and Engineering
Year:
2010

Citing 4
Cited 0

Strategic directions in database systems—breaking out of the box

ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
eSagu™: a data warehouse enabled personalized agricultural advisory system

Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Understanding the dynamics of crop problems by analyzing farm advisory data in eSagu™

DNIS'07 Proceedings of the 5th international conference on Databases in networked information systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

By extending information and communication technologies, a personalised agricultural advisory system called eSagu^TM has been developed and operated for 1,051 cotton farms in the state of Andhra Pradesh, India, during 2004-005. In this system, agricultural experts have delivered expert advice to each farm at regular intervals based on the crop photographs and other information. In this paper, we have carried out cluster/textual analysis experiments on 20,000 advice texts and reported the results on the dynamics of crop problems. The cluster analysis of the advices delivered on each day shows that significant number of farms are suffering from distinct crop production problems. The results also indicate that, a cluster of farms which faces the same crop problem during one week faces distinct crop problems during the subsequent weeks. Based on the results, we can conclude that it is necessary to build farm-specific agricultural advisory systems to reduce crop failures and improve crop productivity.