Multi-scale feature alignment network for 19-class semantic segmentation in agricultural environments

To improve environmental perception and ensure reliable agricultural machinery navigation during field transitions under unstructured farm road conditions, this study utilizes high-resolution RGB camera vision navigation technology to propose a Multi-Scale Feature Alignment Network (MSFA-Net) for 19-class semantic segmentation of agricultural environment, which includes information such as roads, pedestrians, and vehicles. MSFA-Net introduces two key innovations: the DASP module, which integrates multi-scale feature extraction with dual attention mechanisms (spatial and channel), and the MSFA architecture, which enables robust boundary extraction and mitigates interference from lighting variations and obstacles like vegetation. Compared to existing models, MSFA-Net uniquely combines efficient multi-scale feature extraction with real-time inference capabilities, achieving an mIoU of 84.46 % and an mPA of 96.10 %. For 512 × 512 input images, the model processes an average of 26 images/s on a GTX 1650Ti, with a boundary extraction error of less than 0.47 m within 20 m. These results indicate that the proposed MSFA-Net can significantly reduce navigation errors and improve the perception stability of agricultural machinery during field operations. Furthermore, the model can be exported to ONNX or TensorFlow Lite formats, facilitating efficient deployment on embedded devices and existing farm navigation systems. • Developed a semantic segmentation model for agricultural robot navigation. • Designed an end-to-end MSFA-Net for robust image segmentation. • Accurately segments 19 types of farmland objects in complex environments. • Feature enhancement using the DASP module with multi-scale attention.