IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i7p1125-d1433464.html
   My bibliography  Save this article

Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n

Author

Listed:
  • Yongqiang Tian

    (School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China)

  • Chunjiang Zhao

    (National Engineering Research Center for Information Technology in Agriculture, Beijing 100125, China)

  • Taihong Zhang

    (School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Ministry of Education Engineering Research Center for Intelligent Agriculture, Urumqi 830052, China
    Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China)

  • Huarui Wu

    (National Engineering Research Center for Information Technology in Agriculture, Beijing 100125, China
    Key Laboratory of Digital Village Technology, Ministry of Agriculture and Rural Affairs, Beijing 100125, China)

  • Yunjie Zhao

    (School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Ministry of Education Engineering Research Center for Intelligent Agriculture, Urumqi 830052, China
    Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China)

Abstract

To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%—the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:1125-:d:1433464
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/7/1125/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/7/1125/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:1125-:d:1433464. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.