您好，欢迎访问安徽省农业科学院 机构知识库！

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

文献类型： 外文期刊

作者： Zhang, Meng 1 ; Lin, Zichao 1 ; Tang, Shuqi 1 ; Lin, Chenjie 1 ; Zhang, Liping 2 ; Dong, Wei 2 ; Zhong, Nan 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.Anhui Acad Agr Sci, Agr Econ & Informat Res Inst, Hefei 230031, Peoples R China

3.Guangdong Prov Key Lab Agr Artificial Intelligence, Guangzhou 510642, Peoples R China

4.Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti, Guangzhou 510642, Peoples R China

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

页码：

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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：安徽省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台