Rice crop production contributes to the food security of India, more than 40% to overall crop production. Variability from season to season is detrimental to the farmer’s income and livelihoods. Improving the ability of farmers to predict crop productivity. In our method aimed to use of machine learning techniques Support Vector Machine (SVM), Bayesian Networks (BN) and Artificial Neural Networks (ANN) to predict rice production yield and investigate the factors affecting the rice crop yield. Data are sourced from publicly available in Indian Government’s records. The attributes are used for the present studies are rainfall, minimum temperature, average temperature, maximum temperature, area, production and yield . The results showed the accuracy of, SVM is 78.76% , BN is 85.78% and ANN is 97.54% using the WEKA tool. The aim of this study are used evaluated in agriculture for predicting the crop yield production.

Crop yield prediction, Crop analysis Support Vector Machine , Bayesian Networks, Artificial Neural Network

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