Support vector regression methodology for prediction of output energy in rice production

The increase in world population has led to a significant increase in food demand throughout the world, so agricultural policy makers in all countries try to estimate their annual food requirements in advance in order to provide food security for their people. In order to achieve this goal, this study developed a novel predictive model based on the energy inputs employed during the production season. Rice caters more than 30 % of the calorie requirement for the Asian countries. In Iran too rice is one of the most important agricultural products. Therefore, objective of this study was to develop a model based on artificial intelligence for predicting the output energy in rice production. Such a model could help farmers and policy makers. This model employed the polynomial and radial basis function (RBF) as the kernel function for support vector regression (SVR). The input energies from different sources used during rice production were given as the inputs to the model, and the output energy was chosen as the output of the model. In order to achieve generalized performance, SVR sub(poly) and SVR sub(rbf) tried to minimize the generalization error bound, instead of minimizing the training error. The results show that the proposed model improves the predictive accuracy and capability of generalization. Results show that SVRs can serve as a promising alternative for existing prediction models.