## 3 Results
### Analysis of Crop Row Fitting Results  
The model's predictions include the coordinates of the center points of the object bounding boxes, which can be directly used to indicate the locations of individual sugarcane. Given the characteristics of the collected dataset, where crop rows are primarily distributed along the vertical direction, only the x coordinates of the center points were used for clustering. Based on the environmental features of the farmland, the parameters of the DBSCAN clustering algorithm were set as follows: eps = 0.01 and min_samples = 3. Additionally, since edge points unlikely to form complete crop rows were excluded before \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**WTConv** & **C2 PSA\_iRMB** & **UIOU.** & **Precision** & **Recall** & **mAP\({}_{\textbf{50-95}}\)** & **mAP\({}_{\textbf{50-95}}\)** \\ \hline  & & & 81\% & 85.9\% & 88.6\% & 64.5\% \\ ✓ & & & 82.3\% & 86.5\% & 89.5\% & 65.8\% \\  & ✓ & & 81.8\% & 87.5\% & 89.3\% & 65.2\% \\  & & ✓ & 82.9\% & 86.1\% & 89.4\% & 65.4\% \\ ✓ & ✓ & & 82.4\% & 87.5\% & 89.2\% & 66.6\% \\ ✓ & & ✓ & 82.9\% & 87\% & 89.8\% & 67.5\% \\  & ✓ & ✓ & 82.2\% & 87.2\% & 89.8\% & 68.1\% \\ ✓ & ✓ & ✓ & 83\% & 87.8\% & 90.2\% & 69.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments with different modules. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Number of Layers** & **Parameter** & **FLOPs(G)** \\ \hline YOLOv11 & 319 & 2,624,080 & 10.6 \\ \hline YOLOv11n + WTConv & 344 & 2,480,347 & 10 \\ \hline YOLOv11n + & & & \\ C2 PSA\_iRMB & 333 & 2,610,587 & 12.5 \\ \hline YOLOv11n + WTConv + & & & \\ C2 PSA\_iRMB & 358 & 2,489,059 & 13.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computational cost analysis of the new module. clustering, a distance-based thresholding method was applied to remove these edge points before DBSCAN clustering. Based on the clustering results obtained from the DBSCAN algorithm, a smoothed spline curve was employed to fit the centroids of the crop rows. To achieve an optimal balance between fitting accuracy and curve smoothness, the smoothing parameter s was set to 0.1. Experimental observations indicate that, for the dataset used in this study, setting the smoothing parameter s to 0.1 effectively suppresses local noise while avoiding overfitting to individual data points. This configuration enhances the stability and robustness of the curve fitting results. To accommodate potential variations in crop row morphology and enhance the adaptability of the fitting process, order k of the spline curve was adaptively determined based on the number of data points within each cluster. For clusters containing fewer than four points, where the distribution trend tends to approximate a straight line, a first-order spline (k = 1) was employed for linear fitting. In contrast, for clusters with four or more points, where crop rows may exhibit curved or nonlinear patterns, a third-order spline (k = 3) was utilized to more accurately capture the underlying geometric characteristics. To compare the effectiveness of row fitting, this study conducts a comparative analysis using the smoothed spline curve and the least squares method. Due to the irregular spatial distribution and pronounced curvature of sugarcane rows, traditional LSM is insufficient to accurately characterize their overall trajectories. Therefore, this study employs a polynomial fitting method based on the least squares principle as a baseline to compare against the proposed smoothing spline method. Figure 9 shows the fitting performance of different methods. According to the image results, when the data points along the crop rows are densely and continuously distributed, both polynomial fitting and spline curves can accurately capture the overall trend of the crop rows. However, in regions with missing rows, broken rows, or abrupt changes in trend, polynomial fitting often exhibits large oscillations due to its global nature, making it difficult to accurately represent the actual shape.