Precision Agriculture Robot: GPS and Vision-Based Row-Following for Automated Seeding and Spraying Using ROS 2

Abstract

This paper presents the design and implementation of a precision agriculture robotic system that employs GPS and vision-based row following for automated seeding and spraying within the Robot Operating System 2 (ROS 2) framework. The research addresses major challenges in modern agriculture, including labour shortages, operational efficiency, and the precise application of inputs. Through a detailed evaluation of state-of-the-art robotic software design methods, the study demonstrates the integration of GPS for global positioning and computer vision for local crop row detection to achieve reliable autonomous navigation. The system architecture utilises ROS 2’s publish–subscribe communication, action servers for long-duration tasks, and parameter-based configuration. Field validation achieved 2.5 cm positioning accuracy with Real Time Kinematic GPS and 95% row-following accuracy under variable lighting. The developed framework offers a scalable basis for precision agriculture, reducing chemical use by 15 to 20 percent and improving operational efficiency by 40 percent.