Crop Disease Prediction Using Classification Techniques

Agriculture is the backbone of world economy and recent researchers working on improving the crop yield using recent technologies. This article is the one addresses the crop disease prediction using machine learning approaches which detects the disease in crop before it reaches the severity which may result in loss in crop yield. Machine learning and data mining approaches supports the farmers to identify the crop diseases by the features existent in the crop in early stage itself and make them to take necessary steps to reduce the diseases in the crop. This work targets the grass grub insect which creates loss in crop yield. The system uses different existent machine learning approaches and it combines this algorithm to evaluate the efficiency of prediction accuracy. The obtained results shows that the hybrid or ensemble approaches achieve more accuracy than individually used algorithms.