IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i10p1541-d924231.html
   My bibliography  Save this article

Weed Detection in Peanut Fields Based on Machine Vision

Author

Listed:
  • Hui Zhang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Zhi Wang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Yufeng Guo

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Ye Ma

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Wenkai Cao

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Dexin Chen

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Shangbin Yang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Rui Gao

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

Abstract

The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes a weed identification model named EM-YOLOv4-Tiny incorporating multiscale detection and attention mechanisms based on YOLOv4-Tiny. Firstly, an Efficient Channel Attention (ECA) module is added to the Feature Pyramid Network (FPN) of YOLOv4-Tiny to improve the recognition of small target weeds by using the detailed information of shallow features. Secondly, the soft Non-Maximum Suppression (soft-NMS) is used in the output prediction layer to filter the best prediction frames to avoid the problem of missed weed detection caused by overlapping anchor frames. Finally, the Complete Intersection over Union (CIoU) loss is used to replace the original Intersection over Union (IoU) loss so that the model can reach the convergence state faster. The experimental results show that the EM-YOLOv4-Tiny network is 28.7 M in size and takes 10.4 ms to detect a single image, which meets the requirement of real-time weed detection. Meanwhile, the mAP on the test dataset reached 94.54%, which is 6.83%, 4.78%, 6.76%, 4.84%, and 9.64% higher compared with YOLOv4-Tiny, YOLOv4, YOLOv5s, Swin-Transformer, and Faster-RCNN, respectively. The method has much reference value for solving the problem of fast and accurate weed identification in peanut fields.

Suggested Citation

  • Hui Zhang & Zhi Wang & Yufeng Guo & Ye Ma & Wenkai Cao & Dexin Chen & Shangbin Yang & Rui Gao, 2022. "Weed Detection in Peanut Fields Based on Machine Vision," Agriculture, MDPI, vol. 12(10), pages 1-15, September.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1541-:d:924231
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/10/1541/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/10/1541/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    2. Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.
    3. Xinle Zhang & Jian Cui & Huanjun Liu & Yongqi Han & Hongfu Ai & Chang Dong & Jiaru Zhang & Yunxiang Chu, 2023. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    4. Marios Vasileiou & Leonidas Sotirios Kyrgiakos & Christina Kleisiari & Georgios Kleftodimos & George Vlontzos & Hatem Belhouchette & Panos M. Pardalos, 2024. "Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning," Post-Print hal-04297703, HAL.

    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:10:p:1541-:d:924231. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.