In ACRE (Agri-food Competition for Robot Evaluation), <\/strong>autonomous robots compete to demonstrate their ability to perform agricultural tasks (such as removing weeds or surveying crops down to individual-plant resolution) requiring autonomous capabilities (such as discriminating crops from weeds or moving through crops without damaging the plants). These abilities are crucial for the transition of Europe to Agriculture 4.0, where precision agriculture is supported by Artificial Intelligence and Robotics.<\/p>\nAutonomous robots, besides dealing with the most tedious and wearing tasks, enable new and \u201cgreen\u201d ways to perform agricultural operations, greatly reducing and optimizing the use of resources such as water, fertilizers, and pesticides. Indeed, nowadays, human farmers do not have the time to focus on the needs of each individual plant of a crop and to remove weeds one by one. Autonomous robots, which can work 24\/7 without supervision and with extreme precision, can be game-changers by making agriculture more efficient and sustainable at the same time.<\/p>\n
ACRE is a benchmarking competition where the robot are tested in real-world agricultural activities and robot performance is evaluated according to rigorous scientific and metrological criteria, to provide objective and repeatable results. Relevant stakeholders will be able to quantitatively measure progress over time and in comparison to others, and use ACRE results as well-defined technical specifications to certify the performance of their machines.<\/p>\n
ACRE is open both to researchers (who can benefit from its benchmarks and testbeds to validate their results) and to companies (who can exploit the public visibility of ACRE to make their products known to prospective buyers and investors).<\/p>\n
The ACRE evaluation plan describes the competition and its evaluation and will be continuously updated. Additional information will be posted here as well.<\/p>\n
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The ACRE benchmarks<\/b><\/h2>\n
The set of benchmarks prepared by ACRE comprises:<\/p>\n
Plant discrimination<\/b>: decide which plants of a row are weeds and which are crops<\/p>\n
Field navigation<\/b>: move through a cultivation without damaging the crop<\/p>\n
Leaf area estimation<\/b>: estimate the leaf area of the plants along a cultivated row<\/p>\n
Weed destruction<\/b>: destroy unwanted plants (weeds) in intra-row without damaging wanted ones (crops)<\/p>\n
Biomass estimation<\/b>: estimate above-ground crop biomass<\/p>\n
Intra-row weeding<\/b>: perform fully autonomous intra-row weeding of a row (i.e., eliminate the weeds located among the crop plants of a row without damaging the crop)<\/p>\n
Crop mapping<\/b>: produce a map of an entire cultivation by exploring it autonomously<\/p>\n
Among these the final 3 ACRE benchmarks<\/b> will be chosen, according to the requirements of the participants<\/b> to the ACRE dry-run event. The\u00a0 <\/span>1<\/b>st<\/sup><\/b><\/span> ACRE Field Competition <\/b>(Montoldre, June 2021) will have participants compete on the execution of the chosen benchmarks.<\/p>\n<\/div>\n<\/div><\/div>