Determination of Blast Disease Using SVM And ANN Classifiers

Abstract

This paper is mainly industrialized to find the blast disease and reduce the crop defeat and hence increase the paddy cultivation production in an effective manner. In modern farming field, pest and disease identification is a major role of paddy cultivation. Image classification by the use of deep convolutional neural networks of training and methodology used the facilitate a quick and easy system implementation. Pests and diseases are a threat to paddy production, especially in India, but identification remains to be a challenge in massive scale and automatically. The results show that we can effectively detect and recognize the paddy diseases and pests including healthy plant class using classifiers, with the best accuracy of 91%. The significantly high success rate makes the model a really useful advisory or early warning tool, and an approach that would be further expanded to support an unified paddy plant disease identification system to work in real cultivation conditions.