Accurate navigation line recognition in mature soybean fields presents significant challenges due to complex backgrounds. To address this issue, we developed an enhanced YOLOv10n-based model for robust visual navigation, and the assessment was conducted in the experimental fields of the laboratory. The dataset comprised 1363 original images collected at this site and was expanded to 5452 images through data augmentation. The study utilized an innovative data annotation approach focusing on inter-ridge navigation areas to minimize background noise from mature soybean rows. The model was optimized by integrating the CSP Multi-Scale Edge Information Enhancement (CSP-MEIE) module and a lightweight detection head. This architecture significantly improves efficiency, achieving a model size of just 4.5 MB and a parameter count of 2.137 M, while delivering a rapid detection speed of 204.1 FPS. Crucially, the model expands the effective receptive field to 96.6% (t = 99%), far exceeding the 73.0% of the baseline YOLOv10n, ensuring robust feature capture without compromising accuracy (92.6% mAP50-95). For path planning, path points were extracted and predicted using a combination of Kalman filtering and adaptive segmentation. Field experiments demonstrated the system’s effectiveness, achieving an average distance error of 4.53 pixels and an average angle error of 3.57°, a processing speed of 28.17 ms per frame, and a navigation line recognition accuracy of 98.05%. These findings highlight the method’s capability to meet real-time agricultural requirements, offering a reliable visual perception and decision-making basis for autonomous navigation in complex field environments.