Crop Disease Detection and Classification using Transfer Learning and Deep Convolutional Neural Networks

Crop diseases pose a significant threat to global food security, affecting the livelihoods of millions of smallholder farmers. Identifying these diseases is crucial yet challenging due to lack of proper infrastructure. Leveraging the widespread adoption of smartphones and with the advancements in computer vision, this paper proposes a solution through smartphone-assisted disease diagnosis. Utilizing a publicly available dataset comprising of 54,306 images which include both healthy and diseased plants, we introduce a deep convolutional neural network which can identify 14 crop species and 38 diseases. Our final solution achieves an accuracy of 99.2% in training and 95.62% in testing, hence demonstrating the feasibility of the proposed method. Further improvement is possible with the help of a more diverse dataset. Overall, the paper outlines a clear pathway for global crop disease detection through smartphones.