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

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.