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RDRM-YOLO: A High-Accuracy and Lightweight Rice Disease Detection Model for Complex Field Environments Based on Improved YOLOv5

Author

Listed:
  • Pan Li

    (School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China)

  • Jitao Zhou

    (School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China)

  • Huihui Sun

    (School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan 232038, China
    Human-Computer Collaborative Robot Joint Laboratory of Anhui Province, Huainan 232038, China
    College of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

  • Jian Zeng

    (Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Rice leaf diseases critically threaten global rice production by reducing crop yield and quality. Efficient disease detection in complex field environments remains a persistent challenge for sustainable agriculture. Existing deep learning-based methods for rice leaf disease detection struggle with inadequate sensitivity to subtle disease features, high computational complexity, and degraded accuracy under complex field conditions, such as background interference and fine-grained disease variations. To address these limitations, this research aims to develop a lightweight yet high-accuracy detection model tailored for complex field environments that balances computational efficiency with robust performance. We propose RDRM-YOLO, an enhanced YOLOv5-based network, integrating four key improvements: (i) a cross-stage partial network fusion module (Hor-BNFA) is integrated within the backbone network’s feature extraction stage to enhance the model’s ability to capture disease-specific features; (ii) a spatial depth conversion convolution (SPDConv) is introduced to expand the receptive field, enhancing the extraction of fine-grained features, particularly from small disease spots; (iii) SPDConv is also integrated into the neck network, where the standard convolution is replaced with a lightweight GsConv to increase the accuracy of disease localization, category prediction, and inference speed; and (iv) the WIoU Loss function is adopted in place of CIoU Loss to accelerate convergence and enhance detection accuracy. The model is trained and evaluated utilizing a comprehensive dataset of 5930 field-collected and augmented sample images comprising four prevalent rice leaf diseases: bacterial blight, leaf blast, brown spot, and tungro. Experimental results demonstrate that our proposed RDRM-YOLO model achieves state-of-the-art performance with a detection accuracy of 94.3%, and a recall of 89.6%. Furthermore, it achieves a mean Average Precision (mAP) of 93.5%, while maintaining a compact model size of merely 7.9 MB. Compared to Faster R-CNN, YOLOv6, YOLOv7, and YOLOv8 models, the RDRM-YOLO model demonstrates faster convergence and achieves the optimal result values in Precision, Recall, mAP, model size, and inference speed. This work provides a practical solution for real-time rice disease monitoring in agricultural fields, offering a very effective balance between model simplicity and detection performance. The proposed enhancements are readily adaptable to other crop disease detection tasks, thereby contributing to the advancement of precision agriculture technologies.

Suggested Citation

  • Pan Li & Jitao Zhou & Huihui Sun & Jian Zeng, 2025. "RDRM-YOLO: A High-Accuracy and Lightweight Rice Disease Detection Model for Complex Field Environments Based on Improved YOLOv5," Agriculture, MDPI, vol. 15(5), pages 1-27, February.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:479-:d:1597788
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    References listed on IDEAS

    as
    1. Weilun Huang & Xucheng Wang, 2024. "The Impact of Technological Innovations on Agricultural Productivity and Environmental Sustainability in China," Sustainability, MDPI, vol. 16(19), pages 1-37, September.
    2. Li Sun & Jingfa Yao & Hongbo Cao & Haijiang Chen & Guifa Teng, 2024. "Improved YOLOv5 Network for Detection of Peach Blossom Quantity," Agriculture, MDPI, vol. 14(1), pages 1-20, January.
    3. Wenjing Li & Gege Lin & Qingqing Dou & Abbas Ali Chandio & Shahbaz Ali Larik & Yan Liu, 2024. "Can Digital Finance Promote Rice Production? Evidence from Sichuan Province, China," Agriculture, MDPI, vol. 14(6), pages 1-22, June.
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