IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i6p1190-d1163162.html
   My bibliography  Save this article

Detection of Famous Tea Buds Based on Improved YOLOv7 Network

Author

Listed:
  • Yongwei Wang

    (Engineering College, Nanjing Agricultural University, Nanjing 210031, China)

  • Maohua Xiao

    (Engineering College, Nanjing Agricultural University, Nanjing 210031, China)

  • Shu Wang

    (Engineering College, Nanjing Agricultural University, Nanjing 210031, China)

  • Qing Jiang

    (Engineering College, Nanjing Agricultural University, Nanjing 210031, China)

  • Xiaochan Wang

    (Engineering College, Nanjing Agricultural University, Nanjing 210031, China)

  • Yongnian Zhang

    (Engineering College, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

Aiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positions of the enhanced feature extraction network (FPN), and the detection effects of YOLOv7+SE network, YOLOv7+ECA network, YOLOv7+CBAM network and YOLOv7+CA network were compared. It was found that the YOLOv7+CBAM Block model had the highest recognition accuracy with an accuracy of 93.71% and a recall rate of 89.23%. It was found that the model had the advantages of high accuracy and missing rate in small target detection, multi-target detection, occluded target detection and densely distributed target detection. Moreover, the model had good real-time performance and had a good application prospect in intelligent management and automatic harvesting of famous and excellent tea.

Suggested Citation

  • Yongwei Wang & Maohua Xiao & Shu Wang & Qing Jiang & Xiaochan Wang & Yongnian Zhang, 2023. "Detection of Famous Tea Buds Based on Improved YOLOv7 Network," Agriculture, MDPI, vol. 13(6), pages 1-13, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1190-:d:1163162
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/6/1190/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/6/1190/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng Shen & Zhong Tang & Maohua Xiao, 2023. "“Eyes”, “Brain”, “Feet” and “Hands” of Efficient Harvesting Machinery," Agriculture, MDPI, vol. 13(10), pages 1-3, September.

    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:13:y:2023:i:6:p:1190-:d:1163162. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.