Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability

Abstract

Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refinement, particularly in multi-crop classification settings. This study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone. Unlike prior dual-attention frameworks that retain full CBAM and introduce channel-level redundancy, the proposed design isolates complementary spatial and channel mechanisms to improve representational efficiency without increasing computational complexity. The framework is evaluated on controlled multi-crop PlantVillage-derived datasets comprising tomato, potato, pepper, and maize. Across both 3-crop and 4-crop configuration, ATSA-DenseNet consistently outperforms baseline DenseNet121 and single-attention variants, achieving 99.94% accuracy and 0.9994 macro-F1 on the 4-crop setting while maintaining a lightweight footprint (6.96M parameters, 2.87G FLOPs). Grad-CAM visualizations indicate improved localization of disease-relevant regions compared to the baseline. While results are obtained under controlled imaging conditions, the findings demonstrate that redundancy-aware dual-attention enhances feature discrimination efficiency in multi-class agricultural classification tasks. Future work will extend validation to real-field datasets with natural variability.

Plant phenotyping relevance

植物葉の病害状態を画像から推定するCNN手法を提案・評価しており、病害表現型の取得・分類手法が研究の中心である。

Code and data availability

The paper uses PlantVillage-derived and PlantDoc datasets and describes an ATSA-DenseNet model, but no author code, trained checkpoints, dataset links, or public availability statements appear in the supplied blocks. PlantVillage and PlantDoc are cited prior/public datasets, not paper-specific assets. The article's 'No

No evidence-backed public reproduction asset is currently recorded.