In India, around 80% of the people depend on agriculture field. Nowadays the agricultural plants and crop have been affected by the disease caused by insects that transfers the infection to other plants in agriculture. During the infection of these diseases, production level has decreased on the farm. Therefore, it is necessary to identify these infections as the earliest to avoid spread one to another plants. A farmer to find the disease and spreading need more knowledge and adequate experience, so in this situation the computational concept that is Image processing and Machine learning is most useful to the farmer. This paper proposed leaf disease detection by feature extraction using GLCM and Deep convoluted neural network (DCNN) based classification. Then the simulation results show the accuracy, recall, precision and f-1 score as the parameters of the proposed method. This technique will detect the disease symptoms of early stage and helps the farmer to encourage pesticides to avoid the spreading of the disease.

Cashew disease, Image processing, GLCM, DCNN, feature extraction and Classification.