Farmland Visual Navigation with Semantic Segmentation Under Leaf Occlusion

In agricultural machinery visual navigation, accurately identifying the navigation line extraction region (NLER) at the center of the field of view is crucial for obtaining a precise navigation centerline. Although deep learning is the predominant method for NLER extraction, existing approaches face challenges in farmland environments characterized by densely distributed and irregularly extended leaves. These challenges result in unstable predictions, slow inference, and large model sizes that impede real-time applications. To address these issues, we propose a lightweight navigation segmentation residual network (LNS-ResNet), which integrates an inhibition–enhancement module (IEM) and a global convolutional residual block (GCRB). The IEM uses row–column one-dimensional convolutions to enhance vertical features between crop rows and suppress leaf-edge interference, producing more robust input features. The GCRB incorporates a full convolutional global attention (FCGA) mechanism to capture global context while preserving local spatial information. LNS-ResNet effectively reduces foliage interference and achieves accurate segmentation, with intersection over union (IoU) scores of 84.71% for crop row and 93.77% for path regions. Based on the segmentation output, we further propose a mask region determination-based navigation line extraction algorithm (MRD-Line), which directly identifies the NLER and connects the centerline within the mask without relying on line fitting. Deployed experiments on the Jetson TX2 demonstrate that the proposed method achieves both accuracy and efficiency, with mean angular deviations of 0.138${}^{\circ }$ (path) and 0.425${}^{\circ }$ (crop row), with average processing times of 64.1 ms (path) and 62.6 ms (crop row).