Banana Leaf Disease Classification Using Explainable Deep Learning For Farmer-Oriented Advisory System

While banana is an important crop in the agricultural sector of South Asia, fungal and bacterial leaf diseases continue to cause substantial losses and pose a threat to the livelihoods of farmers in the region. The current method of field diagnosis is mostly manually, which is subjective and slow, inconvenient in the field. This paper proposes a rigorous and explainable deep learning framework for the automated banana leaf disease classification that overcomes three major drawbacks of previous works: 1) the lack of comparison of multiple models in similar experimental setups, 2) the lack of statistical validation, and 3) the absence of a connection between the model prediction and the farmer’s guidance. We test the three architectures: a lightweight Custom CNN, ResNet- 50 and EfficientNet-B0, on the publicly available BananaLSD dataset, based on a standardized preprocessing and augmentation pipeline. Stratified 5-fold cross validation is used for assessing the performance and paired t test is used to check the statistical significance of the difference observed. The best classification accuracy of EfficientNet-B0 is 99.69%, the mean cross-validation accuracy is 99.36%, and the standard deviation is the lowest (0.10), showing very high stability. For transparency, we integrate four explainability methods: Grad-CAM, Grad-CAM++, Score- CAM and Layer-CAM, which result in uniform lesion localized heatmaps that validate biologically meaningful feature focus. Finally, an intelligent advisory module translates the predictions in actionable management recommendations directly deployable by agricultural extension services. The proposed framework is built upon predictive excellence, interpretable decision making, and real-world usability in one seamless pipeline, which is critical for advancing precision agriculture.