Enhanced CBAM-LMSPNet: A Lightweight CNN with Learnable Multi-Scale Pooling for Apple Leaf Disease Classification

We propose a lightweight CBAM-enhanced CNN integrated with a novel Learnable Multi-Scale Pooling (LMSP) strategy for apple leaf disease classification. Unlike conventional transfer-learning approaches, the proposed model is trained from scratch and dynamically fuses fine-, medium-, and coarse-scale spatial features to capture complex lesion patterns. Evaluated using stratified 5-fold cross-validation, the proposed network outperforms six benchmark pretrained models, achieving 99.87% test accuracy and a 99.88% macro-F1 score. Furthermore, Grad-CAM++ visualizations demonstrate that the model consistently focuses on disease-relevant lesion regions rather than background areas, enhancing the interpretability of its predictions