A Review on Crop Disease Detection Using Neural Networks and SVM

Abstract

Crop disease identification is that the key to forestall the losses within the yield and quantity of the agricultural product. The studies of the crop diseases mean the studies of visibly observable patterns seen on the crops. For sustainable agriculture health monitoring and disease detection on crops are very critical. It's very difficult to observe crop diseases manually. It requires an incredible amount of labor, expertise in the crops diseases, and also requires excessive processing time. Hence, image processing is employed for the detection of crops diseases. This paper reviewed different techniques of image processing for the detection of crop diseases using their leaf images. It includes ANN, CNN, SVM techniques for disease detection and pre-processing techniques. We found that CNN is the best among them. It needs less pre-processing tasks.