IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1700-d1227308.html
   My bibliography  Save this article

Harvesting Route Detection and Crop Height Estimation Methods for Lodged Farmland Based on AdaBoost

Author

Listed:
  • Yanming Li

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai 200240, China)

  • Yibo Guo

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Liang Gong

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai 200240, China)

  • Chengliang Liu

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai 200240, China)

Abstract

Addressing the challenge of the current harvester route detection method’s reduced robustness within lodging-affected farmland environments and its limited perception of crop lodging, this paper proposes a harvesting operation image segmentation method based on SLIC superpixel segmentation and the AdaBoost ensemble learning algorithm. This segmentation enables two essential tasks. Firstly, the RANSAC algorithm is employed to extract the harvester’s operational route through straight-line fitting from the segmented image. Secondly, the method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester. Experimental results demonstrate the effectiveness of this method in successfully segmenting the harvested and unharvested areas of the farmland. The average angle error for the detected harvesting route is approximately 1.97°, and the average error for crop height detection in the unharvested area is around 0.054 m. Moreover, the algorithm exhibits a total running time of approximately 437 ms. The innovation of this paper lies in its simultaneous implementation of two distinct perception tasks, leveraging the same image segmentation results. This approach offers a robust and effective solution for addressing both route detection and crop height estimation challenges within lodging-affected farmland during harvesting operations.

Suggested Citation

  • Yanming Li & Yibo Guo & Liang Gong & Chengliang Liu, 2023. "Harvesting Route Detection and Crop Height Estimation Methods for Lodged Farmland Based on AdaBoost," Agriculture, MDPI, vol. 13(9), pages 1-18, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1700-:d:1227308
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1700/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/9/1700/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Longfei Zhou & Xiaohe Gu & Shu Cheng & Guijun Yang & Meiyan Shu & Qian Sun, 2020. "Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data," Agriculture, MDPI, vol. 10(5), pages 1-14, May.
    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. Phummarin Thavitchasri & Dechrit Maneetham & Padma Nyoman Crisnapati, 2024. "Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data," Agriculture, MDPI, vol. 14(9), pages 1-21, September.

    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. Jingqian Wen & Yanxin Yin & Yawei Zhang & Zhenglin Pan & Yindong Fan, 2022. "Detection of Wheat Lodging by Binocular Cameras during Harvesting Operation," Agriculture, MDPI, vol. 13(1), pages 1-14, December.
    2. Barbara Dobosz & Dariusz Gozdowski & Jerzy Koronczok & Jan Žukovskis & Elżbieta Wójcik-Gront, 2023. "Evaluation of Maize Crop Damage Using UAV-Based RGB and Multispectral Imagery," Agriculture, MDPI, vol. 13(8), pages 1-14, August.
    3. Yawei Wang & Yifei Chen & Xiangnan Zhang & Wenwen Gong, 2021. "Research on Measurement Method of Leaf Length and Width Based on Point Cloud," Agriculture, MDPI, vol. 11(1), pages 1-13, January.

    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:9:p:1700-:d:1227308. 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.