A predictive modeling approach for improving paddy crop productivity using data mining techniques


Abstract: Agriculture has a great impact on the economy of developing countries. To provide food security for people, there is a need for improving the productivity of major crops. Rapidly changing climatic conditions and the cost of investment in agriculture are major barriers for small-holder farmers. The proposed research aims to develop a predictive model that provides a cultivation plan for farmers to get high yield of paddy crops using data mining techniques. Unlike statistical approaches, data mining techniques extract hidden knowledge through data analysis. The data set used in this research for mining process is real data collected from farmers cultivating paddy along the Thamirabarani river basin. K-means clustering and various decision tree classifiers are applied to meteorological and agronomic data for the paddy crop. The performance of various classifiers is validated and compared. Based on experimentation and evaluation, it has been concluded that the random forest classifier outperforms the other classification methods. Moreover, classification of clustered data provides good classification accuracy. The outcome of this research is the identification of different combination of traits for achieving high yield in paddy crop. The final rules extracted by this research are useful for farmers to make proactive and knowledge-driven decisions before harvest.

Keywords: K-means clustering, decision tree induction, crop productivity, classification accuracy, cultivation plan

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