AgriViTX: An Explainable Vision Transformer Model for Multi-Crop Disease Detection with Farmer-Centric Deployment

Abstract

Crop diseases cause great risks to world food security and livelihood of farmers particularly in the developing nations such as Bangladesh where maize, tomato and onion are staple crops. The conventional manual detection systems are inaccessible and slow with many errors and this highlights the importance of scalable AI-based solutions. This work suggests a deep learning model to perform multi-crop disease detection with Convolutional Neural Networks (CNNs) and Vision Transformer (ViTs). A number of baseline CNN models were compared to a custom ViT architecture where their performance was measured in accuracy, precision, recall, and F1-score. The experiments showed that the ViT was better than CNN baselines with an individual dataset accuracy of 98% on tomato, 96% on onion, and 97% on maize. In the case of the multi-crop classification carried out when the datasets were pooled, the ViT model achieved a higher overall accuracy of 98.7% which shows good generalization across crops. To better interpretability the use of pseudo-segmentation methods was undertaken, where the specific disease-affected areas which are highlighted by the model could be visualized. In addition, an operational web application was created to allow identifying diseases in time when a user uploaded a leaf image, which should be provided as a useful tool to farmers and agricultural advisors. In the course of evaluation, Explainable AI (XAI) tools like LIME, and SHAP were implemented, but their integration into deployed systems is still a matter of future work. Altogether, the study confirms the usefulness of Vision Transformers in terms of strong, explainable, and convenient detection of crop diseases and offers a base in the development of mobile devices that could be used in the future to help farmers to sustain their agriculture.