Chili plant disease detection based on deep convolutional neural network using real time images

Chili is one of the most widely cultivated crop in our home garden. However, Chili diseases becoming more serious when we are not bothered about its symptoms and control measures. The fast detection and diagnosis of plant disease will help to increase the productivity and hence to reduce economic losses. Due to high performance of convolutional neural network, it becomes a general trend to apply them to practical application scenarios. In this paper, proposed a Chili disease recognition model based on Deep CNN. As a part of this work, collected images of 4 types of disease affected and healthy fruits and leaves of chili plant from both network and home garden. Dataset enhancement in order to improve accuracy of CNN is done by data augmentation methods. Using these data set, developed a Deep CNN for detection and identification of diseases. Then increases the number of layers of developed CNN in order to get higher accuracy.