Image-Based Methodology for Accurate Farm Boundary Detection and Geo-Referencing in Autonomous Farming Vehicles

Agriculture is going through a technological revolution, and efforts to improve operational efficiency are being led by automation.This work tackles a major issue facing autonomous farming: the accuracy of farm border recognition, which is constrained by current satellite images. We introduce a novel image-based approach for autonomous farming trucks that combines GPS data and onboard cameras. Our technology streamlines the process from image acquisition to boundary detection and geo-referencing, reducing manual work, and delivering more accurate farm boundary detection than traditional satellite methods. Performance evaluations show that our approach is more accurate and efficient than current practices. This study offers up new possibilities for the advancement of autonomous farming technologies and establishes a new standard for precision agriculture.