ResViT HybridNet: a fusion of ResNet50 and Vision Transformer for banana leaf disease identification

Banana leaf diseases such as Cordana, Sigatoka, and Pestalotiopsis significantly reduce crop yield and quality, necessitating accurate and early detection for effective management. This study proposes ResViT HybridNet, a novel deep learning framework that integrates ResNet50 for spatial feature extraction and a Vision Transformer (ViT) for global context modelling, bridged by a Hybrid Pool Block (HPB) to preserve spatial locality. Using a dataset of 2537 images across four classes, the model achieved an overall accuracy of 99.21%, precision of 0.9843, recall of 0.9921, and F1-score of 0.9881, outperforming conventional CNN and transformer-based models. Extensive ablation and statistical significance analyses confirm the complementary synergy between CNN and transformer components. These results demonstrate that ResViT HybridNet provides a robust and accurate solution for automatic banana leaf disease identification, offering strong potential for deployment in real-world agricultural disease monitoring and crop management systems.