IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i12p2134-d1528661.html
   My bibliography  Save this article

Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s

Author

Listed:
  • Xiuhong Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agricultural and Rural Affairs, Beijing 100083, China)

  • Qingjie Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agricultural and Rural Affairs, Beijing 100083, China)

  • Yichen Qiao

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agricultural and Rural Affairs, Beijing 100083, China)

  • Xinyue Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agricultural and Rural Affairs, Beijing 100083, China)

  • Caiyun Lu

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agricultural and Rural Affairs, Beijing 100083, China)

  • Chao Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agricultural and Rural Affairs, Beijing 100083, China)

Abstract

Straw mulching in conservation tillage farmland can effectively promote land utilization and conservation. However, in this farming mode, surface straw suppresses weed growth, affecting weed size and position distribution and obscuring the weeds, which hampers effective weed management in the field. Accurate weed identification and localization, along with efficient herbicide application, are crucial for achieving precise, efficient, and intelligent precision agriculture. To address these challenges, this study proposes a weed detection model for a targeted spraying system. Firstly, we collected the dataset of weeds in a straw-covered environment. Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. Thirdly, we designed a targeted spraying system by combining the proposed model with the targeted spraying device. Through model test and spraying experiments, the results demonstrated that while the model exhibited a 0.9% decrease in average detection accuracy for weeds, it achieved an 8.46% increase in detection speed, with model memory and computational load reduced by 50.36% and 53.16%, respectively. In the spraying experiments, the proposed method achieved a weed identification accuracy of 90%, a target localization error within 4%, an effective spraying rate of 96.3%, a missed spraying rate of 13.3%, and an erroneous spraying rate of 3.7%. These results confirm the robustness of the model and the feasibility of the targeted spraying method. This approach also promotes the application of deep learning algorithms in precision weed management within directional spraying systems.

Suggested Citation

  • Xiuhong Wang & Qingjie Wang & Yichen Qiao & Xinyue Zhang & Caiyun Lu & Chao Wang, 2024. "Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s," Agriculture, MDPI, vol. 14(12), pages 1-24, November.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2134-:d:1528661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/12/2134/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/12/2134/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huasheng Huang & Jizhong Deng & Yubin Lan & Aqing Yang & Xiaoling Deng & Lei Zhang, 2018. "A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    2. Hailiang Gong & Xi Wang & Weidong Zhuang, 2024. "Research on Real-Time Detection of Maize Seedling Navigation Line Based on Improved YOLOv5s Lightweighting Technology," Agriculture, MDPI, vol. 14(1), pages 1-26, 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. Jinkang Jiao & Ying Zang & Chaowen Chen, 2024. "Key Technologies of Intelligent Weeding for Vegetables: A Review," Agriculture, MDPI, vol. 14(8), pages 1-41, August.
    2. Steven B Kim & Dong Sub Kim & Xiaoming Mo, 2021. "An image segmentation technique with statistical strategies for pesticide efficacy assessment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    3. Nur Adibah Mohidem & Nik Norasma Che’Ya & Abdul Shukor Juraimi & Wan Fazilah Fazlil Ilahi & Muhammad Huzaifah Mohd Roslim & Nursyazyla Sulaiman & Mohammadmehdi Saberioon & Nisfariza Mohd Noor, 2021. "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?," Agriculture, MDPI, vol. 11(10), pages 1-27, October.

    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:12:p:2134-:d:1528661. 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.