IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i8p1369-d1457161.html
   My bibliography  Save this article

A Highly Accurate Detection Platform for Potato Seedling Canopy in Intelligent Agriculture Based on Phased Array LiDAR Technology

Author

Listed:
  • Hewen Tan

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130025, China)

  • Peizhuang Wang

    (Faculty of Mechanical & Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China)

  • Xingwei Yan

    (State Key Laboratory for Strength and Vibration of Mechanical Structures, Department of Engineering Mechanics, Xi’an Jiaotong University, Xi’an 710049, China)

  • Qingqing Xin

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130025, China)

  • Guizhi Mu

    (College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China)

  • Zhaoqin Lv

    (College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China)

Abstract

Precision agriculture, rooted in the principles of intelligent agriculture, plays a pivotal role in fostering a sustainable, healthy, and eco-friendly economy. In order to promote the precision and intelligence of potato seedling management, an innovative platform designed using phased array LiDAR technology was used for precise and accurate detection of potato canopy height. The platform is intricately designed, featuring a suite of components that includes a high-precision rotary encoder, a reliable motor, a robust frame, an inclinometer for precise angle measurements, a computer for data processing, a height adjustment mechanism for adaptability, and an advanced LiDAR system. The LiDAR system is tasked with emitting pulses of laser light toward the canopy of the potato plants, which then scans the canopy to ascertain its height. The result of this scanning process is a rich, three-dimensional point cloud data map that provides a detailed representation of the entire experimental population of potato seedlings. Subsequently, a specialized algorithm for potato seedling canopy height was designed based on integrating the altitude of LiDAR’s installation, the precise measurements from the inclinometer sensor, and the meticulously conducted postprocessing of canopy height data. This algorithm meticulously accounts for a multitude of variables, thereby ensuring a high degree of precision and reliability in the assessment of the potato canopy’s dimensions. The minimum relative error between the measured values of the outdoor canopy height detection platform and the manually measured height is 3.67 ± 0.42%, and the maximum relative error is 8.36 ± 3.47%, respectively. The average relative error is between 3 and 9%, comfortably below the 10% benchmark, which meets the rigorous measurement standards. This platform can efficiently, automatically, and accurately scan the canopy information of potato plants, providing a reference for the automated detection of potato canopy height.

Suggested Citation

  • Hewen Tan & Peizhuang Wang & Xingwei Yan & Qingqing Xin & Guizhi Mu & Zhaoqin Lv, 2024. "A Highly Accurate Detection Platform for Potato Seedling Canopy in Intelligent Agriculture Based on Phased Array LiDAR Technology," Agriculture, MDPI, vol. 14(8), pages 1-16, August.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1369-:d:1457161
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/8/1369/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/8/1369/
    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:14:y:2024:i:8:p:1369-:d:1457161. 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.