Crop Yield Prediction from Soil Parameters through Neupper Rule Established Algorithm - Manimekalai, S; Nandhini, K (International Journal of Engineering and Technology, 2018)
Agriculture is considered as the backbone of the Indian economy. Soil classification is used to maintain and enhance its productivity, to avoid soil degradation and to overcome environmental damage. Repeated Incremental Pruning to Produce Error Reduction (Ripper) was used for crop yield prediction. Though the computational cost of this method is very much decreased, the accuracy is not much increased for prediction of crop yield. Artificial Neural Network (ANN) was widely used technique for yield prediction of crop. But with a large number of hidden layers and nodes, the training time will increase and leads to over fitting of networks. Sometimes, it comes out with hardly interpretation results. Counter Propagation ANN (CP-ANN) was a technique which combining features of both supervised and unsupervised learning technique which effectively predicted the crop yield. However, the prediction accuracy of this method is still poor. In order to overcome the above problems and to improve the crop yield prediction accuracy, a Neupper rule based algorithm is proposed in this paper. The proposed algorithm combines ANN and Ripper classifier algorithms. The soil parameters such as phosphorus (P), potassium (K), sulphur (S), calcium (C), magnesium (Mg), zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn) are given as input to ANN which returns weight of each soil parameter. Then, a decision tree is constructed based on weight values. After this, rules are generated based on the building and optimization stage of Ripper algorithm. The proposed Neupper rule based algorithm improves the accuracy of crop yield prediction by combining ANN and Ripper. Finally, the experiments are carried out in terms of accuracy, precision and Root Mean Square Error (RMSE) to prove the effectiveness of the proposed crop yield prediction method.Â