IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i11p2066-d1522400.html
   My bibliography  Save this article

Weed Detection Algorithms in Rice Fields Based on Improved YOLOv10n

Author

Listed:
  • Yan Li

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Zhonghui Guo

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yan Sun

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Xiaoan Chen

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yingli Cao

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
    Liaoning Key Laboratory of Intelligent Agricultural Technology, Shenyang 110866, China)

Abstract

Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. Therefore, this paper presents an improved weed detection algorithm, YOLOv10n-FCDS (YOLOv10n with FasterNet, CGBlock, Dysample, and Structure of Lightweight Detection Head), using UAV images of Sagittaria trifolia in rice fields as the research object, to address challenges like the detection of small targets, obscured weeds and weeds similar to rice. We enhanced the YOLOv10n model by incorporating FasterNet as the backbone for better small target detection. CGBlock replaced standard convolution and SCDown modules to improve the detection ability of obscured weeds, while DySample enhanced discrimination between weeds and rice. Additionally, we proposed a lightweight detection head based on shared convolution and scale scaling, maintaining accuracy while reducing model parameters. Ablation studies revealed that YOLOv10n-FCDS achieved a 2.6% increase in mean average precision at intersection over union 50% for weed detection, reaching 87.4%. The model also improved small target detection (increasing mAP50 by 2.5%), obscured weed detection (increasing mAP50 by 2.8%), and similar weed detection (increasing mAP50 by 3.0%). In conclusion, YOLOv10n-FCDS enables effective weed detection, supporting variable spraying applications by UAVs in rice fields.

Suggested Citation

  • Yan Li & Zhonghui Guo & Yan Sun & Xiaoan Chen & Yingli Cao, 2024. "Weed Detection Algorithms in Rice Fields Based on Improved YOLOv10n," Agriculture, MDPI, vol. 14(11), pages 1-23, November.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:2066-:d:1522400
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/11/2066/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/11/2066/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Jaehwi Seol & Yonghyun Park & Jeonghyeon Pak & Yuseung Jo & Giwan Lee & Yeongmin Kim & Chanyoung Ju & Ayoung Hong & Hyoung Il Son, 2024. "Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation," Agriculture, MDPI, vol. 14(11), pages 1-17, November.

    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:11:p:2066-:d:1522400. 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.