Abstract: Abatract: This study aims to address two critical challenges faced by agricultural robots in protected tomato cultivation environments: significant light variations and unstable GPS signals that lead to difficulties in rail finding and precise alignment. To enhance autonomy and operational efficiency in such environments, we propose an integrated method combining two-dimensional (2D) LiDAR and an improved YOLOv8-seg network for rail-region segmentation and alignment control. The proposed method consists of three main steps. First, during inter-row navigation, a 2D LiDAR scans the crop rows to obtain point cloud data. An improved DBSCAN clustering algorithm, which adapts to local density variations, is employed to process the point cloud data. This algorithm dynamically adjusts the ε and MinPts parameters based on the local and global density distributions of the point cloud. Combined with Theil-Sen estimation, it fits row lines and enables real-time extraction of the distance and yaw angle of the robot relative to the crop rows. This information is used to control the robot’s navigation along the inter-row path with centimeter-level accuracy. Second, as the robot approaches the target rail region, a side-mounted RGB camera captures images of the rail area. An enhanced YOLOv8-seg network is utilized to identify and segment the rail region. The network incorporates several improvements over the original YOLOv8-seg architecture. A multi-scale fusion module (MSC2f) is introduced to replace the original C2f module. This module uses parallel 3×3 and 5×5 convolutions to capture fine details in bright regions and larger structures in shadowed areas. Additionally, efficient channel attention (ECA) is embedded to suppress glare effects by dynamically adjusting channel weights. To further reduce computational complexity and improve inference speed, depthwise separable convolution and GhostConv modules are implemented. These modifications enable the network to achieve an mAP50 of 96.43%, representing a 1.3 percentage point improvement over the original YOLOv8-seg, while maintaining a lightweight model with only 1.41 million parameters and a frame rate of 96 fps on an RTX 4060 GPU. Finally, based on the segmentation results, the least squares method is used to fit the rail centerline. The lateral deviation between the robot and the rail centerline is calculated, and the robot’s pose is adjusted accordingly. The robot performs a 90° in-place turn and enters the rail region once the deviation is within the acceptable range. Comprehensive experiments were conducted in a multi-span greenhouse at the Lvgang Modern Agricultural Research Institute in Suqian City, Jiangsu Province, where tomato plants were cultivated in coconut coir bags with rail laid between ridges (rail width: 550 mm, adjacent rail spacing: 1760 mm, total rail length: 50 m). A total of 1216 rail images were collected under four typical light conditions (normal light: 8000–25000 lx, strong light: >25000 lx, low light: 2000–8000 lx, local strong light: >25000 lx in local areas), and data augmentation techniques such as dynamic blur, mirror flipping, and brightness adjustment were applied to expand the dataset to 3648 images. Experimental results demonstrate that the proposed method achieves an average lateral deviation of 2.16 cm and a maximum deviation of 4.95 cm under different lighting conditions, with a 100% success rate in 130 trials. The average distance error from the robot center to the crop row, as determined by LiDAR clustering and fitting, is 2.67 cm, and the average heading angle error is 0.16°, validated against data from an Xsens MTi-670 inertial measurement unit (IMU). The improved DBSCAN clustering algorithm achieves a clustering accuracy of 98.07%, which is 1.05 percentage points higher than the traditional DBSCAN algorithm, with a single-frame processing time reduced from 0.0046 s to 0.0017 s (a 63% speedup). In conclusion, the proposed method provides an effective solution for rail-region segmentation and alignment control in protected tomato cultivation environments. By integrating 2D LiDAR and an improved YOLOv8-seg network, the method achieves high precision and robustness in navigation and alignment tasks under varying lighting conditions, offering a promising technical pathway for the development of autonomous agricultural robots capable of operating efficiently in protected environments and potentially extendable to other protected crops such as strawberries and cucumbers, contributing to the advancement of intelligent protected agriculture.