A Generalized Concept for Clustering Capabilities of Weeding Robots

Abstract

ABSTRACT Agriculture is undergoing a significant transformation process with the help of robots. Weeding robots have made their way into the market, and they play a crucial role in automating the weeding process in the field. This study introduces a generalized concept of autonomy levels for currently available weeding robots in the field as well as a comprehensive rating system that allows for a comparison of different weeding robots, irrespective of their developmental stages. We examine the different abilities of market‐available robots, tractor implements, and smart weeding systems when it comes to navigating and recognizing crops and weeds in the field. A technological rating system is employed to rate the robots based on their advantages and critical aspects. To achieve this, we introduce a comparison system based on a measurable ability scale for the three important robot skills navigation, recognition, and target specificity. To demonstrate its applicability, we apply this system of robotic capability clustering to different available weeding system: the market‐available self‐propelled Farmdroid FD 20, the Farming GT, the experimental self‐propelled research platform Bonn Bot, the market‐available smart tractor implement Ecorobotix Ara, and the Bosch BASF Smart Sprayer. We discuss the outlook of interaction models from remote sensing and robots starting from swarm robot aspects to the spot farming, advantages, and limitations of GNSS‐ and vision‐based robots, as well as current challenges for the use of robots in the field and try to answer the question how robots can support farmers in existing workflows.