A hybrid deep learning model for potato leaf disease detection using CNN and vision transformer

Abstract

Finding diseases in potato leaves is important for increasing food yields and lowering losses in agriculture. We describe a strong system that uses the best features of both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to correctly find and group diseases in potato leaves in this study. By combining these two models, our system can successfully pick up both simple and complex patterns in leaf images, which makes it very good at finding diseases. A large set of pictures of potato leaves, including both healthy ones and ones with diseases like Late Blight and Early Blight, were used to train and test the model. Overall, our hybrid model was 97 percent accurate, and its precision, recall, and F1 scores showed that it was good at telling the difference between healthy leaves and those that had diseases. In particular, the model did a great job of finding Late Blight (0.90) and Early Blight (0.96) and it did even better with healthy leaves (0.98). The accuracy of our method is shown by these exact results. We made this answer available by using Flask to create an easy-to-use web application. The app lets users share pictures of potato leaves so that diseases can be found in real time. It gives farmers and agricultural experts immediate feedback, thorough disease information, and treatment suggestions, making it a useful tool for keeping crops healthy. The goal of this study is to connect research with real-life use by combining deep learning methods with useful, scalable solutions. Our work adds to precision agriculture by giving farmers a reliable and effective way to find potato leaf diseases. This leads to better crop management and higher yields.