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An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

Author

Listed:
  • Ning Wang

    (College of Information Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Tingting Qian

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
    Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China)

  • Juan Yang

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
    Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China)

  • Linyi Li

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
    Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China)

  • Yingyu Zhang

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
    Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China)

  • Xiuguo Zheng

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
    Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China)

  • Yeying Xu

    (Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
    Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China)

  • Hanqing Zhao

    (Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China
    College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Jingyin Zhao

    (Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
    Key Laboratory of Intelligent Agricultural Technology (Changjiang Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China
    Shanghai Association of Senior Scientists and Technicians, Shanghai 200070, China)

Abstract

The identification of cucumber fruit is an essential procedure in automated harvesting in greenhouses. In order to enhance the identification ability of object detection models for cucumber fruit harvesting, an extended RGB image dataset ( n = 801) with 3943 positive and negative labels was constructed. Firstly, twelve channels in four color spaces ( RGB , YCbCr , HIS , La*b* ) were compared through the ReliefF method to choose the channel with the highest weight. Secondly, the RGB image dataset was converted to the pseudo-color dataset of the chosen channel ( Cr channel) to pre-train the YOLOv5s model before formal training using the RGB image dataset. Based on this method, the YOLOv5s model was enhanced by the Cr channel. The experimental results show that the cucumber fruit recognition precision of the enhanced YOLOv5s model was increased from 83.7% to 85.19%. Compared with the original YOLOv5s model, the average values of AP , F1 , recall rate, and mAP were increased by 8.03%, 7%, 8.7%, and 8%, respectively. In order to verify the applicability of the pre-training method, ablation experiments were conducted on SSD, Faster R-CNN, and four YOLOv5 versions (s, l, m, x), resulting in the accuracy increasing by 1.51%, 3.09%, 1.49%, 0.63%, 3.15%, and 2.43%, respectively. The results of this study indicate that the Cr channel pre-training method is promising in enhancing cucumber fruit detection in a near-color background.

Suggested Citation

  • Ning Wang & Tingting Qian & Juan Yang & Linyi Li & Yingyu Zhang & Xiuguo Zheng & Yeying Xu & Hanqing Zhao & Jingyin Zhao, 2022. "An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features," Agriculture, MDPI, vol. 12(10), pages 1-15, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1556-:d:926205
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    Citations

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    Cited by:

    1. Yongqiang Tian & Chunjiang Zhao & Taihong Zhang & Huarui Wu & Yunjie Zhao, 2024. "Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n," Agriculture, MDPI, vol. 14(7), pages 1-17, July.
    2. Peichao Cong & Hao Feng & Kunfeng Lv & Jiachao Zhou & Shanda Li, 2023. "MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3," Agriculture, MDPI, vol. 13(2), pages 1-23, February.

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