Dual-Attention-Enhanced MobileViT Network: A Lightweight Model for Rice Disease Identification in Field-Captured Images

文献类型： 外文期刊

第一作者： Zhang, Meng

作者： Zhang, Meng;Lin, Zichao;Tang, Shuqi;Lin, Chenjie;Zhong, Nan;Zhang, Meng;Zhang, Liping;Dong, Wei;Zhang, Meng;Lin, Zichao;Tang, Shuqi;Lin, Chenjie;Zhong, Nan;Zhang, Meng;Lin, Zichao;Tang, Shuqi;Lin, Chenjie;Zhong, Nan

作者机构：

关键词： rice diseases; lightweight model; deep learning; attention mechanism; visualization

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 6 期

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收录情况： SCI

摘要： Accurate identification of rice diseases is crucial for improving rice yield and ensuring food security. In this study, we constructed an image dataset containing six classes of rice diseases captured under real field conditions to address challenges such as complex backgrounds, varying lighting, and symptom similarities. Based on the MobileViT-XXS architecture, we proposed an enhanced model named MobileViT-DAP, which integrates Channel Attention (CA), Efficient Channel Attention (ECA), and PoolFormer blocks to achieve precise classification of rice diseases. The experimental results demonstrated that the improved model achieved superior performance with 0.75 M Params and 0.23 G FLOPs, ensuring computational efficiency while maintaining high classification accuracy. On the testing set, the model achieved an accuracy of 99.61%, a precision of 99.64%, a recall of 99.59%, and a specificity of 99.92%. Compared to traditional lightweight models, MobileViT-DAP showed significant improvements in model complexity, computational efficiency, and classification performance, effectively balancing lightweight design with high accuracy. Furthermore, visualization analysis confirmed that the model's decision-making process primarily relies on lesion-related features, enhancing its interpretability and reliability. This study provides a novel perspective for optimizing plant disease recognition tasks and contributes to improving plant protection strategies, offering a solution for accurate and efficient disease monitoring in agricultural applications.

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