Delineating agricultural field boundaries is essential for applications such as crop management, precision agriculture, land use planning, and crop insurance. Traditionally, this has involved manual digitization from imagery—a process that is both time-consuming and labour-intensive. This deep learning model automates the extraction of agricultural field boundaries from satellite imagery, greatly streamlining the workflow. Its adaptability to different crop types, geographic regions, and imaging conditions makes it ideal for large-scale implementation.