Predicting rice crop yield using Bayesian networks
School of Science
Rice crop production plays a vital role in food security of India, contributing more than 40% to overall crop production. High crop production is dependent on suitable climatic conditions. Detrimental seasonal climate conditions such as low rainfall or temperature extremes can dramatically reduce crop yield. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in important decision making in terms of agronomy and crop choice. This paper reports on the use of Bayesian Networks to predict rice crop yield for Maharashtra state, India. For this study, 27 districts of Maharashtra were selected on the basis of available data from publicly available Indian Government records with various climate and crop parameters selected. The parameters selected for the study were precipitation, minimum temperature, average temperature, maximum temperature, reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. The dataset was processed using the WEKA tool. The classifiers used in the study were BayesNet and NaiveBayes. The experimental results showed that the performance of BayesNet was much better compared with NaiveBayes for the dataset.