## 3 Results and Discussion
### Effect of the Filtering Algorithm on Navigation Angle Under Multiple Scenarios  
To evaluate the effectiveness of the filtering algorithm in optimizing the navigation angle during operation, this study conducted a comparative experiment using 100 randomly selected frames of point cloud data. The data were processed with and without the filtering algorithm, and the results were compared. The average navigation angle and average processing time were used as evaluation metrics to assess the impact of the filtering algorithm on navigation accuracy.  
Figure 12 compares the variation trends of the navigation angle before and after filtering. The black curve represents the raw data with evident high-frequency fluctuations, while the red curve shows a smoother trend after applying the filtering algorithm. Quantitative analysis shows that the angle variance decreased from 0.128\({}^{\circ}\)2 to 0.056\({}^{\circ}\)2, representing a 56.3% reduction. This demonstrates that the filtering algorithm effectively suppresses instantaneous noise and abrupt deviations, significantly improving the stability and continuity of the navigation signal. Such enhancement provides more reliable support for the smooth operation of agricultural machinery in dynamic field environments.  
Table 2 summarizes the average navigation angle and processing time under different field conditions. The results indicate that the variation in the average navigation angle across the three operational scenarios is minimal, ranging from 0.17\({}^{\circ}\) to 0.28\({}^{\circ}\). This suggests that the extracted navigation paths are directionally stable, with good linear consistency and continuity, minimizing the risk of significant deviations. Such stability provides a solid foundation for the reliable navigation of agricultural equipment.  
However, the average processing time varied considerably across different scenarios. In the low-occlusion (S2) and broken-row (S1) conditions, the average processing times were 46.96 ms and 42.36 ms, respectively, indicating high computational efficiency. In contrast, under the high-occlusion condition (S3), the average processing time increased to 75.62 ms, suggesting that the system requires more computational resources to complete crop row segmentation and centerline extraction when facing significant plant occlusion and point cloud noise. These results reveal that occlusion not only affects recognition accuracy but also poses challenges to the system's real-time performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Scenario** & **Average Navigation Angle/\({}^{\circ}\)** & **Average Processing Time \(\pm\) SD/ms** \\ \hline S1 & 0.28 & 42.36 (\(\pm\)20.56) \\ S2 & 0.17 & 46.96 (\(\pm\)19.38) \\ S3 & 0.18 & 75.62 (\(\pm\)21.70) \\ \hline \end{tabular}
\end{table}
Table 2: Average navigation angle during the homework process in different scenarios.  
Figure 12: Average navigation angle.  
In summary, although the navigation angle remained relatively stable across different scenarios, processing efficiency was strongly influenced by environmental complexity. Therefore, enhancing the efficiency of point cloud preprocessing algorithms or incorporating structural optimization strategies will be key to improving overall system performance in high-occlusion environments.