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Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

Author

Listed:
  • Haiqing Wang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Shuqi Shang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Dongwei Wang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Xiaoning He

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Kai Feng

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Hao Zhu

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

Abstract

Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

Suggested Citation

  • Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:931-:d:848882
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    References listed on IDEAS

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    1. Jun Sun & Xiaofei He & Xiao Ge & Xiaohong Wu & Jifeng Shen & Yingying Song, 2018. "Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background," Agriculture, MDPI, vol. 8(12), pages 1-15, December.
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    Cited by:

    1. Hamna Waheed & Waseem Akram & Saif ul Islam & Abdul Hadi & Jalil Boudjadar & Noureen Zafar, 2023. "A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning," Future Internet, MDPI, vol. 15(3), pages 1-23, February.
    2. 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.
    3. Zejin Sun & Hui Yang & Zhifu Zhang & Junxiao Liu & Xirui Zhang, 2022. "An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees," Agriculture, MDPI, vol. 12(9), pages 1-19, August.

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