On the Design of the Agri-Food Competition for Robot Evaluation (ACRE)

The Agri-Food Competition for Robot Evaluation (ACRE) is a novel competition for autonomous robots and smart implements. It is focused on agricultural tasks such as removing weeds or mapping/surveying crops down to single-plant resolution. Such abilities are crucial for the transition to so-called “Agriculture 4.0”, i.e., precision agriculture supported by ICT, Artificial Intelligence, and Robotics. ACRE is a benchmarking competition, i.e., the activities that participants are required to execute are structured as performance benchmarks. The benchmarks are grounded on the key scientific concepts of objective evaluation, repeatability, and reproducibility. Transferring such concepts in the agricultural context, where large parts of the test environment are not fully controllable, is one of the challenges tackled by ACRE. The ACRE competition involves both physical Field Campaigns and data-based Cascade Campaigns. In this paper, we present the benchmarks designed for both kinds of Campaigns and report the outcome of the ACRE dry-runs that took place in 2020.