ENet-CAEM: a field strawberry disease identification model based on improved EfficientNetB0 and multiscale attention mechanism.

Abstract

Introduction Real-time diagnosis of strawberry diseases plays a key role in sustaining yield and improving field management. However, achieving reliable recognition remains challenging. Lesions often display irregular shapes and appear at different scales, which complicates detection. Field images also contain cluttered backgrounds, while many diseases look visually alike, making differentiation more difficult. In addition, collecting data under real conditions is not easy, resulting in small datasets on which deep learning models tend to overfit and fail to generalize. Methods To address these issues, this study introduces ENet-CAEM, a redesigned EfficientNetB0 framework equipped with modules tailored for disease recognition. The Channel Context Module helps the network capture key lesion features while suppressing background noise. The Multi-Scale Efficient Channel Attention module applies multiple one-dimensional filters of varying sizes in parallel, enabling the model to highlight critical patterns, tell apart similar diseases, and adapt to lesions of different scales. A lightweight version of Atrous Spatial Pyramid Pooling is further integrated, allowing the network to perceive features at multiple spatial ranges. To balance local detail with global context, a mixed pooling strategy is adopted, enhancing robustness when lesion shapes change. Finally, Learnable DropPath and label smoothing are applied as regularization strategies, reducing overfitting and improving generalization on limited data. Results Experiments show that ENet-CAEM achieves 85.84% accuracy on a self-built dataset, outperforming the baseline by 4.29%. On a public strawberry dataset, the model reaches 97.39%, surpassing existing approaches. Discussion The proposed ENet-CAEM model shows superior accuracy and robustness over existing methods, providing an effective solution for strawberry disease recognition in practical field environments.

Plant phenotyping relevance

イチゴ葉の病斑・病害状態を画像から認識する深層学習手法を開発し、複数データセットで性能評価しているため、植物病害表現型の取得・推定が中心である。

Code and data availability

The supplied blocks describe a self-built strawberry disease image dataset (1,486 images collected in Hohhot) and model details, but contain no public data deposit, no author code release, and no data availability statement with a URL. The public strawberry dataset mentioned is cited prior work, not a paper-specific de

No evidence-backed public reproduction asset is currently recorded.