IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i3p428-d774656.html
   My bibliography  Save this article

Drip-Tape-Following Approach Based on Machine Vision for a Two-Wheeled Robot Trailer in Strip Farming

Author

Listed:
  • Chung-Liang Chang

    (Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan)

  • Hung-Wen Chen

    (Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan)

  • Yung-Hsiang Chen

    (Department of Vehicle Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan)

  • Chang-Chen Yu

    (Department of Vehicle Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan)

Abstract

Due to the complex environment in the field, using machine vision technology to enable the robot to travel autonomously was a challenging task. This study investigates a method based on mathematical morphology and Hough transformation for drip tape following by a two-wheeled robot trailer. First, an image processing technique was utilized to extract the drip tape in the image, including the selection of the region of interest (ROI), Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) color space conversion, color channel selection, Otsu’s binarization, and morphological operations. The line segments were obtained from the extracted drip tapes image by a Hough line transform operation. Next, the deviation angle between the line segment and the vertical line in the center of the image was estimated through the two-dimensional law of cosines. The steering control system could adjust the rotation speed of the left and right wheels of the robot to reduce the deviation angle, so that the robot could stably travel along the drip tape, including turning. The guiding performance was evaluated on the test path formed by a drip tape in the field. The experimental results show that the proposed method could achieve an average line detection rate of 97.3% and an average lateral error of 2.6 ± 1.1 cm, which was superior to other drip-tape-following methods combined with edge detection, such as Canny and Laplacian.

Suggested Citation

  • Chung-Liang Chang & Hung-Wen Chen & Yung-Hsiang Chen & Chang-Chen Yu, 2022. "Drip-Tape-Following Approach Based on Machine Vision for a Two-Wheeled Robot Trailer in Strip Farming," Agriculture, MDPI, vol. 12(3), pages 1-18, March.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:428-:d:774656
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/3/428/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/3/428/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chung-Liang Chang & Bo-Xuan Xie & Sheng-Cheng Chung, 2021. "Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm," Agriculture, MDPI, vol. 11(11), pages 1-21, October.
    2. David Reiser & El-Sayed Sehsah & Oliver Bumann & Jörg Morhard & Hans W. Griepentrog, 2019. "Development of an Autonomous Electric Robot Implement for Intra-Row Weeding in Vineyards," Agriculture, MDPI, vol. 9(1), pages 1-12, January.
    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. Karla L. Gage & Lauren M. Schwartz-Lazaro, 2019. "Shifting the Paradigm: An Ecological Systems Approach to Weed Management," Agriculture, MDPI, vol. 9(8), pages 1-17, August.
    2. Mustafa Ucgul & Chung-Liang Chang, 2023. "Design and Application of Agricultural Equipment in Tillage Systems," Agriculture, MDPI, vol. 13(4), pages 1-3, March.
    3. Huimin Fang & Gaowei Xu & Xinyu Xue & Mengmeng Niu & Lu Qiao, 2022. "Study of Mechanical-Chemical Synergistic Weeding on Characterization of Weed–Soil Complex and Weed Control Efficacy," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    4. Ahmed Kayad & Dimitrios S. Paraforos & Francesco Marinello & Spyros Fountas, 2020. "Latest Advances in Sensor Applications in Agriculture," Agriculture, MDPI, vol. 10(8), pages 1-8, August.

    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:3:p:428-:d:774656. 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.