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MSGV-YOLOv7: A Lightweight Pineapple Detection Method

Author

Listed:
  • Rihong Zhang

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Zejun Huang

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Yuling Zhang

    (Shantou Agricultural Product Quality and Safety Center, Shantou 515071, China)

  • Zhong Xue

    (South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang 524091, China)

  • Xiaomin Li

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

Abstract

In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The enhancements in these architectures have significantly improved the ability of feature extraction and fusion, thereby speeding up the detection rate. Empirical results indicated that MSGV-YOLOv7 surpassed the original YOLOv7 with a 1.98% increase in precision, 1.35% increase in recall rate, and 3.03% increase in mAP , while the real-time detection speed reached 17.52 frames per second. Compared with Faster R-CNN and YOLOv5n, the mAP of this model increased by 14.89% and 5.22%, respectively, while the real-time detection speed increased by approximately 2.18 times and 1.58 times, respectively. The application of image visualization testing has verified the results, confirming that the MSGV-YOLOv7 model successfully and precisely identified the unique features of pineapples. The proposed pineapple detection method presents significant potential for broad-scale implementation. It is expected to notably reduce both the time and economic costs associated with pineapple harvesting operations.

Suggested Citation

  • Rihong Zhang & Zejun Huang & Yuling Zhang & Zhong Xue & Xiaomin Li, 2023. "MSGV-YOLOv7: A Lightweight Pineapple Detection Method," Agriculture, MDPI, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:29-:d:1306288
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    References listed on IDEAS

    as
    1. Zuhui Huang & Qiao Liang, 2018. "Agricultural organizations and the role of farmer cooperatives in China since 1978: past and future," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(1), pages 48-64, February.
    2. Huawei Yang & Yinzeng Liu & Shaowei Wang & Huixing Qu & Ning Li & Jie Wu & Yinfa Yan & Hongjian Zhang & Jinxing Wang & Jianfeng Qiu, 2023. "Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model," Agriculture, MDPI, vol. 13(7), pages 1-21, June.
    3. Zuhui Huang & Qiao Liang, 2018. "Agricultural organizations and the role of farmer cooperatives in China since 1978: past and future," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(1), pages 48-64, February.
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