Application of Deep CNN for image-based identification and classification of plant diseases

Plant leaf disease detection has a significant contribution in preventing impeded growth of plants which results in low-and poor-quality yield. Agriculture has a significant role in Indian economy as well as for many other countries. Hence, it is required to prevent quantity reduction and quality degradation which are a result of plant diseases. In the past, farmers identified and monitored plant ailments with naked eyes. It required significant expertise for identification of disease-and disease-causing agent. In recent years with advanced technology, using automated techniques of smart farming, plant disease identification and diagnosis can be easily done. It is very beneficial in case of big farms where lot of monitoring work is significantly reduced. Also, the disease can be identified at a very early stage when first symptoms appear on leaves. This paper explores the potential of deep learning for detection of plant disease. This method involves use of image processing with deep learning so as to obtain a clear image or useful information. Further, Convolutional Neural Network (CNN) with input layer, output layer, and hidden layers is used for image classification.