关键词: 甘蔗, 杂草识别, YOLOv8n算法, 目标检测

Abstract: Weed is one of the most important factors affecting sugarcane growth, in order to recognize different sugarcane weeds, a deep learning‑based weed detection method for sugarcane was proposed. Firstly, images were collected with weeds that were common and harmful to sugarcane growth in the Guangxi region, and the images were then enhanced with operations such as panning, flipping, cropping, shrinking, contrast and brightness adjustment and denoising. Following that, the dataset was tested with the YOLOv8n detection model and compared to the YOLOv3—tiny, YOLOv4—tiny, YOLOv5n, and Yolov7—tiny detection models. The experimental results show that the YOLOv8n detection model has a 98.3% precision, a 96.8% recall, and a 98.2% mAP. Precision is improved by 26.9%, 12.7%, 4.2%, and 9%, respectively, when compared to the current mainstream lightweight target detection algorithms YOLOv3—tiny, YOLOv4—tiny, YOLOv5s, and YOLOv7—tiny. The recall is improved by 25%, 24.5%, 6.1%, and 10.6%, respectively, and the mAP is improved by 25.9%, 19.4%, 3.6%, and 7.8%. Meanwhile, the YOLOv8n detection model can recognize sugarcane weeds with high accuracy in dense, occluded, interspersed weeds, small targets, and dim environments.

Key words: sugarcane, weed identification, YOLOv8 algorithm, object detection