IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i11p1049-d664859.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/11/1049/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/11/1049/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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. 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.
    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.

    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. LoPiccalo, Katherine, 2022. "Impact of broadband penetration on U.S. Farm productivity: A panel approach," Telecommunications Policy, Elsevier, vol. 46(9).
    2. Nathan D. DeLay & Nathanael M. Thompson & James R. Mintert, 2022. "Precision agriculture technology adoption and technical efficiency," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 195-219, February.
    3. Peng Wang & Jiang Liu & Lijia Xu & Peng Huang & Xiong Luo & Yan Hu & Zhiliang Kang, 2021. "Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    4. Khanna, Madhu, 2021. "Digital Transformation for a Sustainable Agriculture: Opportunities and Challenges," 2021 Conference, August 17-31, 2021, Virtual 315052, International Association of Agricultural Economists.
    5. Hrozencik, Aaron & Aillery, Marcel, 2021. "Trends in U.S. Irrigated Agriculture: Increasing Resilience Under Water Supply Scarcity," Economic Information Bulletin 327359, United States Department of Agriculture, Economic Research Service.
    6. Julian M. Alston & Philip G. Pardey, 2020. "Innovation, Growth, and Structural Change in American Agriculture," NBER Chapters, in: The Role of Innovation and Entrepreneurship in Economic Growth, pages 123-165, National Bureau of Economic Research, Inc.
    7. Piotr Boniecki & Maciej Zaborowicz & Agnieszka Pilarska & Hanna Piekarska-Boniecka, 2020. "Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN," Agriculture, MDPI, vol. 10(6), pages 1-9, June.
    8. Margot Luyckx & Leonie Reins, 2022. "The Future of Farming: The (Non)-Sense of Big Data Predictive Tools for Sustainable EU Agriculture," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    9. Douglas Gollin & Christopher Udry, 2021. "Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture," Journal of Political Economy, University of Chicago Press, vol. 129(1), pages 1-80.
    10. Madhu Khanna & Shady S. Atallah & Saurajyoti Kar & Bijay Sharma & Linghui Wu & Chengzheng Yu & Girish Chowdhary & Chinmay Soman & Kaiyu Guan, 2022. "Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges," Agricultural Economics, International Association of Agricultural Economists, vol. 53(6), pages 924-937, November.
    11. Kolady, Deepthi E. & Van Der Sluis, Evert, 2021. "Adoption Determinants of Precision Agriculture Technologies and Conservation Agriculture: Evidence from South Dakota," Western Economics Forum, Western Agricultural Economics Association, vol. 19(2), December.
    12. Stefania Troiano & Matteo Carzedda & Francesco Marangon, 2023. "Better richer than environmentally friendly? Describing preferences toward and factors affecting precision agriculture adoption in Italy," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-15, December.
    13. Weidong Zhu & Jun Sun & Simin Wang & Jifeng Shen & Kaifeng Yang & Xin Zhou, 2022. "Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
    14. J Blasch & B van der Kroon & P van Beukering & R Munster & S Fabiani & P Nino & S Vanino, 2022. "Farmer preferences for adopting precision farming technologies: a case study from Italy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 33-81.
    15. Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
    16. Hrozencik, Aaron & Aillery, Marcel, 2021. "Trends in U.S. Irrigated Agriculture: Increasing Resilience Under Water Supply Scarcity," USDA Miscellaneous 316792, United States Department of Agriculture.
    17. Masoud Yazdanpanah & Kurt Klein & Tahereh Zobeidi & Stefan Sieber & Katharina Löhr, 2022. "Why Have Economic Incentives Failed to Convince Farmers to Adopt Drip Irrigation in Southwestern Iran?," Sustainability, MDPI, vol. 14(4), pages 1-15, February.
    18. Dhoubhadel, Sunil P., 2020. "Precision Agriculture Technologies and Farm Profitability," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304229, Agricultural and Applied Economics Association.
    19. Fausti, Scott W. & Erickson, Bruce & Clay, David E. & Clay, Sharon A., 2021. "Is the Custom Service Industry’s Role in Precision Agriculture Linked to Workforce Development?," Western Economics Forum, Western Agricultural Economics Association, vol. 19(2), December.
    20. Wang, Sun Ling & Olver, Ryan & Bonin, Daniel & Dodson, Laura L. & Williams, Ryan C., 2022. "Climate change, technology adoption, and field crop farm productivity in the United States: Short-term vs. long-term," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322595, Agricultural and Applied Economics Association.

    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:11:y:2021:i:11:p:1049-:d:664859. 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.