Analysing dynamics of crop problems by applying text analysis methods on farm advisory data of eSaguTM Online publication date: Wed, 10-Nov-2010
by R. Uday Kiran, P. Krishna Reddy, M. Kumara Swamy, G. Syamasundar Reddy
International Journal of Computational Science and Engineering (IJCSE), Vol. 5, No. 2, 2010
Abstract: By extending information and communication technologies, a personalised agricultural advisory system called eSaguTM 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.
Online publication date: Wed, 10-Nov-2010
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