Accurate identification of the sugarcane tail tip is crucial for the real-time automation control of the harvester’s cutting device, improving harvesting efficiency, and reducing impurity rates. This paper proposes Slim-YOLO, an improved YOLO11n-based algorithm incorporating a lightweight RepViT backbone, an ELANSlimNeck neck structure, and the Unified-IoU (UIoU) loss function. Experimental results on the sugarcane tailing dataset show that Slim-YOLO achieves an mAP50 of 92.2% and mAP50:95 of 48.2%, outperforming …