IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i8p817-d1631320.html
   My bibliography  Save this article

Pruning Branch Recognition and Pruning Point Localization for Walnut ( Juglans regia L.) Trees Based on Point Cloud Semantic Segmentation

Author

Listed:
  • Wei Zhu

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Xiaopeng Bai

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Daochun Xu

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Wenbin Li

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

Abstract

Intelligent pruning technology is significant in reducing management costs and improving operational efficiency. In this study, a branch recognition and pruning point localization method was proposed for dormant walnut ( Juglans regia L.) trees. First, 3D point clouds of walnut trees were reconstructed from multi-view images using Neural Radiance Fields (NeRFs). Second, Walnut-PointNet was improved to segment the walnut tree into Trunk, Branch, and Calibration categories. Next, individual pruning branches were extracted by cluster analysis and pruning rules were adjusted by classifying branches based on length. Finally, Principal Component Analysis (PCA) was used for length extraction, and pruning points were determined based on pruning rules. Walnut-PointNet achieved an OA of 93.39%, an ACC of 95.29%, and an mIoU of 0.912 on the walnut tree dataset. The mean absolute errors in length extraction for the short-growing branch group and the water sprout were 28.04 mm and 50.11 mm, respectively. The average success rate of pruning point recognition reached 89.33%, and the total time for pruning branch recognition and pruning point localization for the entire tree was approximately 16 s. This study provides support for the development of intelligent pruning for walnut trees.

Suggested Citation

  • Wei Zhu & Xiaopeng Bai & Daochun Xu & Wenbin Li, 2025. "Pruning Branch Recognition and Pruning Point Localization for Walnut ( Juglans regia L.) Trees Based on Point Cloud Semantic Segmentation," Agriculture, MDPI, vol. 15(8), pages 1-22, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:817-:d:1631320
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/8/817/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/8/817/
    Download Restriction: no
    ---><---

    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:15:y:2025:i:8:p:817-:d:1631320. 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.