## 3 Results  
### SugarRow-YOLO Training Result  
The SugarRow-YOLO model was systematically evaluated after 200 training epochs. Figure 7 illustrates the performance trends of SugarRow-YOLO and YOLOv11n throughout the training process. As the number of training epochs increases, both models exhibit significant improvements in precision, recall, mAP\({}_{50}\), and mAP\({}_{50-95}\), with all metrics gradually stabilizing after approximately the 100 th epoch. During the early stages of training, the values of precision, recall, mAP\({}_{50}\), and mAP\({}_{50-95}\) remain relatively low, indicating that the model is still in the initial phase of feature learning and parameter optimization. As training progresses, the model gradually captures more discriminative feature representations, leading to continuous improvements in performance metrics. In the later stages, the fluctuations in these metrics tend to stabilize, suggesting that the model has largely converged, demonstrates good robustness, and exhibits strong object detection capability. After 200 training epochs, the SugarRow-YOLO model achieves a precision of 83%, a recall of 87.8%, and mAP\({}_{50}\) and mAP\({}_{50-95}\) values of 90.2% and 69.2%, respectively. Compared to YOLOv11n, these results represent improvements of 2.0%, 1.9%, 1.6%, and 4.7%, respectively, thereby validating the effectiveness of the proposed method.  
Figure 8 presents representative detection results from the test set. Panel (a) depicts complex scenarios including missing and discontinuous crop rows, as well as inter-row weed interference. Panel (b) illustrates a case of row closure caused by dense crop growth on the right side of the image. In most scenarios, both YOLOv11n and SugarRow-YOLO accurately identify sugarcane targets. However, YOLOv11n exhibits a noticeable degree of missed detections and false positives, whereas SugarRow-YOLO demonstrates more robust performance under challenging conditions. Specifically, in panel (a) of Figure 8, YOLOv11n incorrectly identifies weeds in regions with missing or broken crop rows as sugarcane, whereas SugarRow-YOLO effectively avoids such misclassifications. Both models exhibit some degree of missed detection of sugarcane; however, YOLOv11n is more prone to falsely detecting weeds as sugarcane, which may interfere with the subsequent crop row fitting process. Additionally, in the presence of sugarcane seedlings within the rows, YOLOv11n shows a tendency toward underdetection. In panel (b), YOLOv11n demonstrates a high rate of missed and false detections in densely populated crop regions, particularly near the edges. In contrast, SugarRow-YOLO effectively mitigates these issues. YOLOv11n frequently fails to accurately detect crop targets exhibiting significant morphological variations, whereas SugarRow-YOLO successfully identifies such instances. However, it occasionally segments a single crop target into multiple bounding boxes, introducing some false positives. Nonetheless, this segmentation has minimal impact on subsequent crop row identification. Overall, SugarRow-YOLO exhibits superior detection performance under complex field conditions and is better suited for sugarcane row detection tasks.  
Figure 7: Training process curves of SugarRow-YOLO and YOLOv11n.