IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i2p303-d1338408.html
   My bibliography  Save this article

Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios

Author

Listed:
  • Juanli Jing

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China)

  • Menglin Zhai

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China)

  • Shiqing Dou

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China)

  • Lin Wang

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China)

  • Binghai Lou

    (Guangxi Academy of Specialty Crops, Guilin 541004, China)

  • Jichi Yan

    (College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China)

  • Shixin Yuan

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China)

Abstract

The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This network introduces a BiFormer bilevel routing attention mechanism, which replaces regular convolution with GSConv, adds the VoVGSCSP module to the neck network, and replaces the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv) in the backbone network. The improved model significantly reduces the number of model parameters and the model inference time, while maintaining the network’s high recognition rate for citrus fruits. The results showed that the fruit recognition accuracy of the modified model was 97.9% on the test dataset. Compared with the YOLOv7-tiny, the number of parameters and the size of the improved network were reduced by 38.47% and 4.6 MB, respectively. Moreover, the recognition accuracy, frames per second (FPS), and F1 score improved by 0.9, 2.02, and 1%, respectively. The network model proposed in this paper has an accuracy of 97.9% even after the parameters are reduced by 38.47%, and the model size is only 7.7 MB, which provides a new idea for the development of a lightweight target detection model.

Suggested Citation

  • Juanli Jing & Menglin Zhai & Shiqing Dou & Lin Wang & Binghai Lou & Jichi Yan & Shixin Yuan, 2024. "Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios," Agriculture, MDPI, vol. 14(2), pages 1-16, February.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:303-:d:1338408
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/2/303/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/2/303/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zijia Yang & Hailin Feng & Yaoping Ruan & Xiang Weng, 2023. "Tea Tree Pest Detection Algorithm Based on Improved Yolov7-Tiny," Agriculture, MDPI, vol. 13(5), pages 1-22, May.
    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. 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.
    2. Yaxin Wang & Xinyuan Liu & Fanzhen Wang & Dongyue Ren & Yang Li & Zhimin Mu & Shide Li & Yongcheng Jiang, 2023. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    3. Wenji Yang & Xiaoying Qiu, 2024. "A Novel Crop Pest Detection Model Based on YOLOv5," Agriculture, MDPI, vol. 14(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:2:p:303-:d:1338408. 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.