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Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation

Author

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  • Yanxin Hu

    (School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China)

  • Gang Liu

    (School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
    Jilin Province Data Service Industry Public Technology Research Centre, Changchun 130102, China)

  • Zhiyu Chen

    (School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
    Jilin Province Data Service Industry Public Technology Research Centre, Changchun 130102, China)

  • Jiaqi Liu

    (School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China)

  • Jianwei Guo

    (School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China)

Abstract

Maize is one of the world’s most important crops, and maize leaf diseases can have a direct impact on maize yields. Although deep learning-based detection methods have been applied to maize leaf disease detection, it is difficult to guarantee detection accuracy when using a lightweight detection model. Considering the above problems, we propose a lightweight detection algorithm based on improved YOLOv5s. First, the Faster-C3 module is proposed to replace the original CSP module in YOLOv5s, to significantly reduce the number of parameters in the feature extraction process. Second, CoordConv and improved CARAFE are introduced into the neck network, to improve the refinement of location information during feature fusion and to refine richer semantic information in the downsampling process. Finally, the channel-wise knowledge distillation method is used in model training to improve the detection accuracy without increasing the number of model parameters. In a maize leaf disease detection dataset (containing five leaf diseases and a total of 12,957 images), our proposed algorithm had 15.5% less parameters than YOLOv5s, while the mAP(0.5) and mAP(0.5:0.95) were 3.8% and 1.5% higher, respectively. The experiments demonstrated the effectiveness of the method proposed in this study and provided theoretical and technical support for the automated detection of maize leaf diseases.

Suggested Citation

  • Yanxin Hu & Gang Liu & Zhiyu Chen & Jiaqi Liu & Jianwei Guo, 2023. "Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation," Agriculture, MDPI, vol. 13(9), pages 1-22, August.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1664-:d:1223182
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    References listed on IDEAS

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    1. Poonam Dhiman & Amandeep Kaur & V. R. Balasaraswathi & Yonis Gulzar & Ali A. Alwan & Yasir Hamid, 2023. "Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    2. Lei Du & Yaqin Sun & Shuo Chen & Jiedong Feng & Yindi Zhao & Zhigang Yan & Xuewei Zhang & Yuchen Bian, 2022. "A Novel Object Detection Model Based on Faster R-CNN for Spodoptera frugiperda According to Feeding Trace of Corn Leaves," Agriculture, MDPI, vol. 12(2), pages 1-21, February.
    3. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    4. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    5. Yonis Gulzar, 2023. "Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    6. Xiaoyu Li & Yuefeng Du & Lin Yao & Jun Wu & Lei Liu, 2021. "Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm," Agriculture, MDPI, vol. 11(12), pages 1-17, December.
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    Cited by:

    1. Yi Shao & Wenzhong Yang & Jiajia Wang & Zhifeng Lu & Meng Zhang & Danny Chen, 2024. "Cotton Disease Recognition Method in Natural Environment Based on Convolutional Neural Network," Agriculture, MDPI, vol. 14(9), pages 1-21, September.
    2. Ping Dong & Kuo Li & Ming Wang & Feitao Li & Wei Guo & Haiping Si, 2023. "Maize Leaf Compound Disease Recognition Based on Attention Mechanism," Agriculture, MDPI, vol. 14(1), pages 1-22, December.

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