Detection and mapping of crop weeds and litter for agricultural robots

Abstract

Agricultural robotics is a fast-spreading research field. Using robots to help or substitute human workers presents numerous advantages. Many tasks, like field monitoring and harvesting, are relatively simple but time-consuming. Instead, robots can perform these tasks with high precision and without interruptions, guaranteeing a continuous analysis of the field and a constant stream of information delivered to the farmers. The availability of such capillary information can be exploited to increase the efficiency of the soil and decrease the need for pesticides. The development of a robust platform for autonomous field navigation and monitoring is the first step toward these goals. We propose a pipeline to control a small robot in a crop field without the need for expensive sensors, such as RTK-GPS or 3D lidars. Additionally, we present an algorithm for the detection and mapping of weeds and undesired objects such as litter, proving the capability of the system to autonomously monitor the state of the field while traversing it.