Accurate tea leaf disease classification in real-world scenarios is hindered by complex backgrounds and the loss of fine-grained lesion details during CNN down sampling.To address this,we propose ResNet50-Dual-Fusion....Accurate tea leaf disease classification in real-world scenarios is hindered by complex backgrounds and the loss of fine-grained lesion details during CNN down sampling.To address this,we propose ResNet50-Dual-Fusion.It integrates a Cross-Attention Feature Fusion module(CAmodule)to adaptively reconstruct tiny lesion edges via cross-spatial interaction between shallow and deep features.Furthermore,a Magnitude-Aware Linear Attention(MALA)module with 2D Rotary Position Embedding(RoPE)is introduced to rectify magnitude neglect,effectively suppressing background noise.Evaluated on a 5,276-image dataset,our model achieves 85.96%accuracy(+3.00%over the baseline),outperforming architectures like ViT and Swin-Tiny.Grad-CAM visualizations confirm its superior lesion localization,providing a robust paradigm for automated crop disease diagnosis.展开更多
文摘Accurate tea leaf disease classification in real-world scenarios is hindered by complex backgrounds and the loss of fine-grained lesion details during CNN down sampling.To address this,we propose ResNet50-Dual-Fusion.It integrates a Cross-Attention Feature Fusion module(CAmodule)to adaptively reconstruct tiny lesion edges via cross-spatial interaction between shallow and deep features.Furthermore,a Magnitude-Aware Linear Attention(MALA)module with 2D Rotary Position Embedding(RoPE)is introduced to rectify magnitude neglect,effectively suppressing background noise.Evaluated on a 5,276-image dataset,our model achieves 85.96%accuracy(+3.00%over the baseline),outperforming architectures like ViT and Swin-Tiny.Grad-CAM visualizations confirm its superior lesion localization,providing a robust paradigm for automated crop disease diagnosis.
