The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors' keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019).INDEX TERMS Agriculture 4.0, bibliometrics, climate-smart agriculture, digital agriculture, literature review, precision agriculture.
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.
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