Scholarlink

<p>Robotic weeding has been attracting attention to reduce the burden of weeding in agriculture. In weeding by a robot, it is required to detect weeds correctly. In addition, it is expected that robots can remove weeds without damaging surrounding crops by detecting the stems of weeds and approaching the stems. In this paper, YOLO11, an instance segmentation method, is combined with rule-based image processing based on weed shape features to achieve a real-time stem detection method. The proposed method uses YOLO11 instance segmentation to detect the contour of the weed region, and then uses convexity defects in the weed region to detect the stem position. The proposed method was applied to a dataset taken at a farm, and both Precision and Recall of the stem detection achieved 0.886.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Stem detection using YOLO11 and convexity defects of leaf shape in weed image

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

查看全文
  • Seito Takeuchi,
  • Shuyan Liu,
  • Shunsuke Komizunai,
  • Taku Senoo,
  • Atsushi Konno

摘要

Robotic weeding has been attracting attention to reduce the burden of weeding in agriculture. In weeding by a robot, it is required to detect weeds correctly. In addition, it is expected that robots can remove weeds without damaging surrounding crops by detecting the stems of weeds and approaching the stems. In this paper, YOLO11, an instance segmentation method, is combined with rule-based image processing based on weed shape features to achieve a real-time stem detection method. The proposed method uses YOLO11 instance segmentation to detect the contour of the weed region, and then uses convexity defects in the weed region to detect the stem position. The proposed method was applied to a dataset taken at a farm, and both Precision and Recall of the stem detection achieved 0.886.