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Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm

Author

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  • Chung-Liang Chang

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

  • Bo-Xuan Xie

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

  • Sheng-Cheng Chung

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

Abstract

This paper presents a mechanical control method for precise weeding based on deep learning. Deep convolutional neural network was used to identify and locate weeds. A special modular weeder was designed, which can be installed on the rear of a mobile platform. An inverted pyramid-shaped weeding tool equipped in the modular weeder can shovel out weeds without being contaminated by soil. The weed detection and control method was implemented on an embedded system with a high-speed graphics processing unit and integrated with the weeder. The experimental results showed that even if the speed of the mobile platform reaches 20 cm/s, the weeds can still be accurately detected and the position of the weeds can be located by the system. Moreover, the weeding mechanism can successfully shovel out the roots of the weeds. The proposed weeder has been tested in the field, and its performance and weed coverage have been verified to be precise for weeding.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:11:p:1049-:d:664859
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    References listed on IDEAS

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    1. Jun Sun & Xiaofei He & Xiao Ge & Xiaohong Wu & Jifeng Shen & Yingying Song, 2018. "Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background," Agriculture, MDPI, vol. 8(12), pages 1-15, December.
    2. Schimmelpfennig, David, 2016. "Farm Profits and Adoption of Precision Agriculture," Economic Research Report 249773, United States Department of Agriculture, Economic Research Service.
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    Cited by:

    1. Mustafa Ucgul & Chung-Liang Chang, 2023. "Design and Application of Agricultural Equipment in Tillage Systems," Agriculture, MDPI, vol. 13(4), pages 1-3, March.
    2. 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.
    3. 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.

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