IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i12p2039-d987041.html
   My bibliography  Save this article

3D Positioning Method for Pineapple Eyes Based on Multiangle Image Stereo-Matching

Author

Listed:
  • Anwen Liu

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China)

  • Yang Xiang

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China)

  • Yajun Li

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China
    Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Zhengfang Hu

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China)

  • Xiufeng Dai

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China)

  • Xiangming Lei

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China)

  • Zhenhui Tang

    (College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China)

Abstract

Currently, pineapple processing is a primarily manual task, with high labor costs and low operational efficiency. The ability to precisely detect and locate pineapple eyes is critical to achieving automated pineapple eye removal. In this paper, machine vision and automatic control technology are used to build a pineapple eye recognition and positioning test platform, using the YOLOv5l target detection algorithm to quickly identify pineapple eye images. A 3D localization algorithm based on multiangle image matching is used to obtain the 3D position information of pineapple eyes, and the CNC precision motion system is used to pierce the probe into each pineapple eye to verify the effect of the recognition and positioning algorithm. The recognition experimental results demonstrate that the mAP reached 98%, and the average time required to detect one pineapple eye image was 0.015 s. According to the probe test results, the average deviation between the actual center of the pineapple eye and the penetration position of the probe was 1.01 mm, the maximum was 2.17 mm, and the root mean square value was 1.09 mm, which meets the positioning accuracy requirements in actual pineapple eye-removal operations.

Suggested Citation

  • Anwen Liu & Yang Xiang & Yajun Li & Zhengfang Hu & Xiufeng Dai & Xiangming Lei & Zhenhui Tang, 2022. "3D Positioning Method for Pineapple Eyes Based on Multiangle Image Stereo-Matching," Agriculture, MDPI, vol. 12(12), pages 1-17, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2039-:d:987041
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/12/2039/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/12/2039/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chuandong Zhang & Huali Ding & Qinfeng Shi & Yunfei Wang, 2022. "Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network," Agriculture, MDPI, vol. 12(8), pages 1-12, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.

    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. Zejin Sun & Hui Yang & Zhifu Zhang & Junxiao Liu & Xirui Zhang, 2022. "An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
    2. Xingmei Xu & Lu Wang & Xuewen Liang & Lei Zhou & Youjia Chen & Puyu Feng & Helong Yu & Yuntao Ma, 2023. "Maize Seedling Leave Counting Based on Semi-Supervised Learning and UAV RGB Images," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    3. Anlan Ding & Baoliang Peng & Ke Yang & Yanhua Zhang & Xiaoxuan Yang & Xiuguo Zou & Zhangqing Zhu, 2022. "Design of a Machine Vision-Based Automatic Digging Depth Control System for Garlic Combine Harvester," Agriculture, MDPI, vol. 12(12), pages 1-19, December.

    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:12:y:2022:i:12:p:2039-:d:987041. 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.