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YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

Author

Listed:
  • Liming Qin

    (Research Institute of Motor and Intelligent Control Technology, Taizhou University, Taizhou 318000, China)

  • Zheng Xu

    (Research Institute of Motor and Intelligent Control Technology, Taizhou University, Taizhou 318000, China)

  • Wenhao Wang

    (School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China)

  • Xuefeng Wu

    (School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China)

Abstract

In recent years, rapeseed oil has received considerable attention in the agricultural sector, experiencing appreciable growth. However, weed-related challenges are hindering the expansion of rapeseed production. This paper outlines the development of an intelligent weed detection and laser weeding system—a non-chemical and precision agricultural protection method of weeding Veronica didyma in winter rapeseed fields in the Yangtze River Basin. A total of 234 Veronica didyma images were obtained to compile a database for a deep-learning model, and YOLOv7 was used as the detection model for training. The effectiveness of the model was demonstrated, with a final accuracy of 94.94%, a recall of 95.65%, and a mAP@0.5 of 0.972 obtained. Subsequently, parallel-axis binocular cameras were selected as the image acquisition platform, with binocular calibration and semi-global block matching used to locate Veronica didyma within a cultivation box, yielding a minimum confidence and camera height values of 70% and 30 cm, respectively. The intelligent weed detection and laser weeding system was then built, and the experimental results indicated that laser weeding was practicable with a 100 W power and an 80 mm/s scanning speed, resulting in visibly lost activity in Veronica didyma and no resprouting within 15 days of weeding. The successful execution of Veronica didyma detection and laser weeding provides a new reference for the precision agricultural protection of rapeseed in winter and holds promise for its practical application in agricultural settings.

Suggested Citation

  • Liming Qin & Zheng Xu & Wenhao Wang & Xuefeng Wu, 2024. "YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields," Agriculture, MDPI, vol. 14(6), pages 1-14, June.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:6:p:910-:d:1411603
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