Android application for crop disease diagnosis using image processing and deep learning (Smart Agriculture).

Abstract

Plant diseases are a major threat to food security worldwide. An accurate and a faster approach to detection and diagnosis of diseases in crops will go a long way to help farmers save their crop and increase yield. Recent developments in smartphone technology and deep neural networks have allowed researchers to develop accurate and ease to use systems to help farmer in this regard. In this dissertation, we developed an android based cotton crop disease detector using deep convolutional networks and image processing. We made use of transfer learning using a pre trained Inceptionv3 model. Additional layers were added to the pretrained model and trained on our dataset. The trained model finally integrated into an android mobile app and experimental results on the developed model were able to achieve an average accuracy of 83%.