## 4 Conclusions  
This study proposes a LiDAR-based method for crop row detection and navigation line extraction tailored to soybean-maize strip intercropping systems. The method consists of four main steps: point cloud preprocessing, crop row identification, feature point clustering, and navigation line extraction. The experimental results demonstrate that the proposed approach effectively identifies crop rows and extracts navigation paths under various field conditions.  
Under the low-occlusion condition (S2), the system achieved superior performance, with a crop row detection accuracy of 90%, an average navigation angle deviation of 0.17\({}^{\circ}\), and a processing time of 46.96 ms--demonstrating both high precision and real-time efficiency. In the broken-row and missing-plant scenario (S1), detection accuracy declined to 81% due to disrupted row continuity. Despite significant leaf occlusion in scenario S3, the system maintained an accuracy of 84%, highlighting its resilience under visually complex conditions. Lateral deviation tests further substantiated the system's reliability. In S2, the average deviation was 5.76 cm, with a maximum of 11.95 cm; although deviations increased in S1 and S3, they remained within acceptable operational thresholds. Collectively, these results confirm the method's robustness and adaptability across diverse and challenging field scenarios.  
In summary, the proposed crop row detection and navigation line extraction algorithm enables accurate path planning for soybean-maize strip intercropping systems. Although performance varied slightly under complex field conditions, the system exhibited strong adaptability and robustness. Future work will focus on enhancing system resilience under high-occlusion scenarios by optimizing point cloud preprocessing and refining feature extraction algorithms. In addition, multi-sensor fusion strategies--such as the integration of LiDAR, RGB cameras, and inertial measurement units (IMUs)--will be explored. Potential fusion methods include Kalman filtering for real-time data alignment and deep learning-based approaches (e.g., CNN-LiDAR fusion) for improved spatial perception. Furthermore, strategies to address the broken-row problem will be developed to ensure consistent and reliable navigation even in discontinuous or sparse planting conditions.  
**Author Contributions:** M.O.: conceptualization, methodology, writing--review and editing, and supervision. R.Y.: investigation and writing--original draft. Y.W.: investigation and formal analysis. Y.G.: investigation, formal analysis, and writing--original draft. M.W.: investigation and validation. X.D.: investigation. W.J.: conceptualization and supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Plan of China (2023 YFD2000503) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD-2023-87).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors thank the Faculty of Agricultural Equipment of Jiangsu University for its facilities and support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.