## 3 Results and Discussion  
### Crop Row Detection and Navigation Line Extraction  
To evaluate the adaptability of the proposed crop row detection and navigation line extraction algorithm under different field conditions, point cloud processing and analysis were conducted in three typical scenarios. The results are presented in Figure 10. All experiments were conducted on a workstation running the Windows 11 operating system equipped with an Intel Core i7-14700F processor and an NVIDIA GeForce RTX 4060 Ti graphics card (Lenovo, Beijing, China). Figure 10a shows the point cloud data of soybean and maize crop rows under a broken-row and missing-plant condition, labeled scenario S1. Apparent discontinuities in the crop rows can be observed, reflecting the challenging conditions of this test case. Figure 10b corresponds to a low-occlusion scenario, labeled S2, where the crop row structures are clearly defined, facilitating accurate navigation path extraction. Figure 10c illustrates a high-occlusion scenario, in which the ground surface is almost entirely covered by soybean foliage, significantly increasing the difficulty of crop row detection and path planning. Figure 10: Point cloud data collected from a real soybean–maize strip intercropping field under different scenarios. (**a**) Point cloud data under the broken-row and missing-plant scenarios; (**b**) point cloud data under the low-occlusion scenario; (**c**) point cloud data under the high-occlusion scenario. Figure 9: Navigation line extraction. In this study, the accuracy of crop row detection and navigation line extraction under actual field operating conditions was experimentally validated. To ensure that agricultural machinery can accurately navigate along crop rows during field operations, 100 frames of point cloud data were randomly selected from each test scenario for processing and analysis. The average extraction accuracy of the navigation lines in each frame was calculated using Equation (10). The experimental results are presented in Figure 11 and Table 1.  
\[\theta=arctan(m_{0}) \tag{13}\]  
In the equation, the navigation angle \(\theta\) represents the angle between the forward direction of the agricultural machinery and the extracted navigation line. Table 1 presents the navigation line extraction results under different field scenarios. The proposed method achieved robust performance in all three conditions, with accuracies above 80%. Scenario S2 (low occlusion) yielded the highest accuracy (90%) due to uniform crop distribution and clearly distinguishable ground features, which enabled more effective point cloud segmentation and line extraction. In contrast, scenario S1 exhibited reduced accuracy (81%) caused by discontinuous crop rows and uneven point cloud density. Scenario S3 faced the most significant occlusion from soybean leaves, leading to partial ground-level data loss and a moderate accuracy of 84%. These results underscore the challenges of high-occlusion environments, where poor visibility and structural overlap can compromise vision-based navigation accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Average Soybean Plant**} & \multicolumn{2}{c}{**Average Maize Plant**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-5}  & \multicolumn{1}{c}{**Height/cm**} & & & \multicolumn{1}{c}{**Height/cm**} & \\ \cline{2-5} \cline{7-7} \multirow{-2}{*}{**Scenario**} & **Tested** & **Actual** & **Tested** & **Actual** & **Accuracy/\%** \\  & **Value** & **Value** & **Value** & **Value** & \\ \hline S1 & 10 & 14.86 & 20 & 22.67 & 81 \\ S2 & 30 & 31.71 & 50 & 45.14 & 90 \\ S3 & 40 & 44.94 & 75 & 77.69 & 84 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Navigation line extraction results. Figure 11: Crop row detection and navigation line extraction under different field conditions.