A Hybrid Multi-Backbone Deep Learning Framework with Learnable Attention Fusion for Explainable Potato Leaf Disease Detection

Potato leaf diseases, particularly early blight and late blight, significantly reduce crop yield and quality, making accurate and timely diagnosis essential. This paper introduces a hybrid multi-backbone deep learning framework, which combines EfficientNet-V2-S, Swin Transformer and ViT-Base, and introduces a Learnable Attention Fusion (LAF) module to capture complementary local and global feature representations. A progressive layer-freezing approach is used to enhance transfer learning and model generalization. The proposed framework is tested in terms of 5-fold cross validation and cross dataset testing. The experimental results are obtained with good average classification accuracy of 99.47% for 5-fold cross validation and 97.34% for cross-dataset evaluation, exhibiting good robustness and generalization capability for different data distributions. Moreover, Grad-CAM is used to give visual interpretation of model predictions by identifying the relevant areas of the image associated with the disease. The proposed model is used as a web application for practical agricultural applications, allowing identification of potato leaf diseases in real-time based on uploaded leaf images. From the experimental results, it is proved that the proposed framework gives an accurate, robust and interpretable solution for the potato leaf disease automatic classification.