Voting Classification Method with Clustering Method for the Plant Disease Detection

The economy of the country is largely dependent on the production of the agricultural field. Thus, it is essential to detect the diseases of plant at primary phase for maximizing the agriculture yield. The automated methods are assisted in detecting the diseases at initial phase and providing more accuracy. The disease is detected after the starting of appearance of symptoms on the leaves of the plant using automated methods. This work introduced an automated method for detecting the disease of plant on the basis of 4 tasks such as to pre-process the image, segment an image, extract the features and classify the disease. The literature survey conducted on diverse methods, which the researchers suggested already, is also considered in this paper. The symptoms of infected leaf are analyzed using GLCM algorithm and the voting classifier is presented to classify the disease. In this classifier, DT, SVM and K-NN techniques are integrated that will lead to enhance the accuracy for detecting the diseases in advance