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

Meng Zhang, Zichao Lin, Shuqi Tang, Chenjie Lin, Liping Zhang, Wei Dong and Nan Zhong ()
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Meng Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zichao Lin: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Shuqi Tang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chenjie Lin: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Liping Zhang: Agricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
Wei Dong: Agricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
Nan Zhong: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 6, 1-22

Abstract: 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.

Keywords: rice diseases; lightweight model; deep learning; attention mechanism; visualization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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