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MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection

Author

Listed:
  • Liangquan Jia

    (School of Information Engineering, Huzhou University, Huzhou 313000, China
    These authors contributed equally to this work.)

  • Tao Wang

    (School of Information Engineering, Huzhou University, Huzhou 313000, China
    These authors contributed equally to this work.)

  • Yi Chen

    (School of Arts and Science, Fujian Medical University, Fuzhou 350122, China)

  • Ying Zang

    (School of Information Engineering, Huzhou University, Huzhou 313000, China)

  • Xiangge Li

    (School of Information Engineering, Huzhou University, Huzhou 313000, China)

  • Haojie Shi

    (College of Modern Agriculture, Zhejiang A&F University, Hangzhou 311300, China)

  • Lu Gao

    (School of Information Engineering, Huzhou University, Huzhou 313000, China)

Abstract

The efficient identification of rice pests and diseases is crucial for preventing crop damage. To address the limitations of traditional manual detection methods and machine learning-based approaches, a new rice pest and disease recognition model based on an improved YOLOv7 algorithm has been developed. The model utilizes the lightweight network MobileNetV3 for feature extraction, reducing parameterization, and incorporates the coordinate attention mechanism (CA) and the SIoU loss function for enhanced accuracy. The model has been tested on a dataset of 3773 rice pest and disease images, achieving an accuracy of 92.3% and an mAP@.5 of 93.7%. The proposed MobileNet-CA-YOLO model is a high-performance and lightweight solution for rice pest and disease detection, providing accurate and timely results for farmers and researchers.

Suggested Citation

  • Liangquan Jia & Tao Wang & Yi Chen & Ying Zang & Xiangge Li & Haojie Shi & Lu Gao, 2023. "MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection," Agriculture, MDPI, vol. 13(7), pages 1-18, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1285-:d:1177182
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    Cited by:

    1. Yuzhe Bai & Fengjun Hou & Xinyuan Fan & Weifan Lin & Jinghan Lu & Junyu Zhou & Dongchen Fan & Lin Li, 2023. "A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques," Agriculture, MDPI, vol. 13(9), pages 1-23, September.

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