Abstract: Autonomous navigation is required for high recognition accuracy, real-time performance, and environmental adaptability under complex ground textures and ambiguous path boundaries in orchards. In this study, a lightweight improved U-Net path recognition model, named MEG-UNet, was proposed to extract navigation lines for multi-scenario orchards. A navigation line generation algorithm was developed to incorporate with a traversability determination mechanism. In the MEG-UNet model, MobileNetV4 was first adopted as the encoder backbone to reduce computational complexity for feature extraction. Haar wavelet downsampling was introduced into the deep feature extraction to enhance the perception of global path structures and continuity. Subsequently, Ghost Convolution was employed in the decoder to further reduce the computational cost for effective feature reconstruction. Moreover, a Spatial-Channel Collaborative Efficient Local Attention (SCC-ELA) mechanism was embedded into the skip connection module. Spatial and channel attention features were integrated to improve the path segmentation accuracy in a multi-scenario orchard. The mechanism also guided the network to focus on critical path regions. A navigation line generation algorithm was designed with a traversability decision. A binary path image was obtained to extract the traversable region at the bottom of the image. The path width was estimated after extraction. The path traversability was determined by comparing the path width with the dimensions of agricultural machinery. Once the path was determined to be traversable, edge detection was performed to generate a midpoint sequence. Subsequently, B-spline curve fitting was applied to generate the final navigation line. Furthermore, a pipeline parallel processing architecture was introduced using a dual-buffer mechanism in collaborative processing between path recognition and navigation line generation. Experiments were conducted in multiple orchards, including plain apple orchards, hilly apple orchards, vineyards, and greenhouse strawberry orchards. The path recognition model achieved higher segmentation accuracy than classical semantic segmentation and YOLO-series models. Compared with the baseline ResNet50-UNet model, the mean Intersection over Union (mIoU) and mean Pixel Accuracy (mPA) improved by 5.27 percentage points and 2.96 percentage points, respectively, among different orchard scenarios. In the two-apple orchards, the mIoU values reached 93.06% and 92.37%, and the mPA values reached 96.85% and 95.97%, respectively. In vineyard and strawberry orchards, the mIoU values were 92.03% and 91.80%, while the mPA values were 96.32% and 96.05%, respectively. Only 8.32 million parameters were able to fully meet the requirements of the lightweight deployment. In addition, the navigation line generation algorithm achieved the maximum traversability determination accuracy of 98%, and the navigation line extraction completeness exceeded 90%. The average angular error of the B-spline-fitted navigation lines ranged from 0.37°to 2.45°, the maximum angular error was 3.26°, and the maximum distance error was within 7.52cm. The overall algorithm operated stably in both video-stream processing experiments and field tests, with the processing frame rate above 25 frames per second. The MEG-UNet improved path segmentation accuracy in complex orchards, indicating its low computational cost. Combined with the traversability determination mechanism and B-spline-based navigation line generation, reliable path recognition and navigation guidance can be expected for autonomous agricultural equipment. The real-time performance and environmental adaptability can also provide effective technical support for visual navigation in multi-scenario orchard environments.