## 4 Discussion  
In this study, an enhanced object detection model, SugarRow-YOLO, was proposed to address the challenges of severe leaf-stem overlap and dense target occlusion commonly observed during the sugarcane elongation stage. Experimental results demonstrate that the proposed method outperforms the baseline YOLOv11n model across several key performance metrics, thereby validating the effectiveness and practical relevance of the proposed improvements. Furthermore, to accommodate the high variability in sugarcane row spacing, accurate crop row fitting was achieved by integrating DBSCAN clustering with a smoothing spline curve, based on the detection results. In terms of sugarcane detection, the proposed SugarRow-YOLO model demonstrates superior detection accuracy and robustness compared with other mainstream recognition models. First, the incorporation of the WTConv module effectively expands the receptive field and enhances computational efficiency, enabling the model to maintain strong detec \begin{table}
\begin{tabular}{c c c c c} \hline
**Actual Rows** & **Number of** & **Correctly** & & \\
**per Image** & **Images** & **Recognized Rows** & **Total Rows** & **Accuracy (\%)** \\ \hline \(\geq\)19 & 20 & 380 & 390 & 97.4\% \\ \(<\)19 & 20 & 333 & 348 & 95.7\% \\ All & 40 & 713 & 738 & 96.6\% \\ \hline \end{tabular}
\end{table} 
Table 6: Detection accuracy of sugarcane rows. Figure 9: Demonstration of smoothed spline curve and polynomial of crop rows under different conditions, including overall fitting performance and detailed views. (**a**) smoothed spline curve; (**b**) polynomial fitting. tion performance even in complex background conditions. Second, the iRMB attention mechanism improves the model's ability to capture spatial structural features, offering enhanced robustness when dealing with morphological variations and complex structural patterns in sugarcane rows. Additionally, the UIOU loss function exhibits clear advantages in recognizing densely overlapping targets, significantly reducing both false positives and missed detections. However, in scenarios where sugarcane plants exhibit severe leaf-stem overlap and large morphological differences, the target boundaries may become indistinct, causing the model to potentially misinterpret a single plant as multiple objects. This issue may arise from the current methodology lacking dedicated modules specifically designed to handle occlusion and dense object detection. Despite this limitation, SugarRow-YOLO maintains high detection accuracy overall. The predicted results still effectively capture the spatial distribution trends of the crops, ensuring that the subsequent fitting of crop row centerlines is not adversely affected. To address this challenge, future work will consider introducing occlusion-aware feature enhancement modules or context aggregation mechanisms to explicitly model occlusion relationships. To better evaluate the robustness and cross-domain generalizability of SugarRow-YOLO, we directly evaluated the trained model on the dataset proposed by [PERSON] et al. [35]. This dataset was collected in soybean fields under different lighting conditions. It covers multiple growth stages and includes various weed species. Identifying these weeds accurately makes the dataset particularly challenging for recognition tasks. SugarRow-YOLO achieved a precision of only 25.8% when evaluated on this challenging dataset, indicating that the current model's generalizability is still limited. To address this limitation, future work will focus on employing domain adaptation techniques [36] and data fusion methods [37] to enhance the model's generalization capability. In the task of crop row extraction, the DBSCAN clustering combined with spline curve fitting, as proposed in this study and based on the target detection results, overcomes the limitations of traditional methods that rely on assumptions of uniform planting patterns. This approach effectively adapts to the real-world variability in sugarcane row and plant spacing.