## 3 Results and Discussion
### Influence of Multi-Scenario Conditions on the Navigation Line Extraction Algorithm  
To further validate the reliability and robustness of the proposed navigation line extraction algorithm under multiple scenarios, Equation (14) was used to calculate the lateral deviation during the navigation process. Corresponding deviation curves were plotted to visualize the navigation performance in different conditions, including broken-row and missing-plant scenarios, low-occlusion scenarios, and high-occlusion scenarios. \[d=\frac{|m_{0}x_{0}+y_{0}+(y_{2}-m_{0}x_{2})|}{\sqrt{m_{0}^{2}+1}} \tag{14}\]  
As shown in the data in Figure 13, the maximum lateral deviation under the low-occlusion scenario (S2) was the smallest, reaching only 11.95 cm. However, the minimum and average lateral deviations were relatively larger, at 0.40 cm and 5.76 cm, respectively, indicating that the S2 scenario effectively reduced the influence of external factors, resulting in more concentrated and stable data. In contrast, although the overall average deviations in the broken-row (S1) and high-occlusion (S3) scenarios were slightly higher, the data exhibited relatively stable trends. Table 3 presents the lateral deviation performance of the system under different levels of field occlusion. The results show that the average lateral deviation in the S2 (low-occlusion) scenario was 5.76 cm, which is significantly lower than the 7.55 cm observed in the S1 (broken-row) scenario, with a difference of 1.79 cm. The S3 (high-occlusion) scenario also showed a lower average deviation of 6.34 cm, 1.21 cm less than that of S1. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Test Scenario**} & \multirow{2}{*}{\begin{tabular}{c} **Maximum Lateral** \\ **Deviation/cm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Minimum Lateral** \\ **Deviation/cm** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average Lateral** \\ **Deviation/cm** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Standard** \\ **Deviation/cm** \\ \end{tabular} } \\ \hline S1 & 14.65 & 0.55 & 7.55 & 4.07 \\ S2 & 11.95 & 0.40 & 5.76 & 3.56 \\ S3 & 14.60 & 0.20 & 6.34 & 4.66 \\ \hline \end{tabular}
\end{table} 
Table 3: Lateral deviation in the process of motion in different scenarios. Figure 13: Lateral deviation during operation under different scenarios. (**a**) Lateral deviation under the broken-row and missing-plant scenarios; (**b**) lateral deviation under the low-occlusion scenario; (**c**) lateral deviation under the high-occlusion scenario. This trend suggests that, compared to the path discontinuity caused by structural breaks and missing plants, the perceptual interference introduced by occlusion has a milder impact on system navigation control. Broken rows interrupt the continuity of the crop structure, making path prediction more difficult and leading to greater deviations. In contrast, although the high-occlusion environment involves significant leaf coverage, the spatial distribution of the crops remains coherent, allowing the system to rely on residual structural features for effective detection and navigation correction. Moreover, the maximum deviation (11.95 cm) and standard deviation (3.56 cm) in the S2 scenario were also lower than those in S1 and S3, further confirming the advantage of low-occlusion environments in improving path stability. While the S3 scenario exhibited a slightly larger deviation range, the system maintained an acceptable average error level even in complex point cloud conditions, demonstrating strong robustness. In summary, the experimental results confirm that the proposed navigation system can maintain high path accuracy and operational stability under various crop morphologies and occlusion conditions. The system performed especially well under low-occlusion environments, validating the accuracy and environmental adaptability of the proposed crop row detection and navigation line extraction algorithm.