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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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