Autonomous mapping and monitoring of rumex with mobile robot

Weed control is important in agriculture and gardening but typically employed manual solutions are both time and labour consuming. By combining advanced developments in digitalization and AI (artificial intelligence), this paper presents two autonomous weed-control related processes: i) for the mapping of rumex weed in an unexplored agricultural field and ii) for the surveillance of the marked herbs. The processes employ a mobile robot equipped with a LIDAR sensor (mainly used for navigation and mapping) and a front stereo camera. A YOLOv8 object detector which is specifically trained for rumex detection is used to locate rumex plants in the camera image. For the first application of mapping, the weeds are marked on the map of the field while for the second task of surveillance, the Ant Colony Optimization looks for an efficient route connecting all those marked weeds which allows the robot to revisit and monitor them. The proposed processes are validated under both real experiments and simulation in realistic Gazebo environments.