Cybele, High Performance Computing´s potential for precision agriculture and livestock farming

Cybele 0

From 28 to 30 January the Rectory of the Universitat Politècnica de Catalunya (Polytechnic University of Catalunya) hosted the kickoff meeting of the CYBELE project (Fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics).

Coordinated by the Waterford Institute of Technology (WIT) and involving 31 international partners, the 14-million-euro CYBELE project is financed under Horizon 2020 (H2020) - The EU Framework Programme for Research and Innovation.

CYBELE has a 3-year timeframe to show how the convergence of HPC, Big Data analysis, cloud computing and IoT could revolutionize agriculture, boosting foodstuff supply and reducing food scarcity, generating social, economic and environmental benefits. CYBELE aims to ensure that the various stakeholders have unmediated, integrated access to a vast store of large-scale datasets of diverse types from various sources, doing so in such a way as to generate value and extract useful insights. The idea is to afford secure access to large-scale HPC infrastructure that supports data discovery, processing, combination and visualization services, solving modeling challenges that call for a high computing power.

Cybele I

In this project GMV is leading one of the nine pilots to assess and demonstrate the use of these technologies as applied to precision agriculture and livestock farming, focusing on the development of climate services as decision-making support systems for orchard management. In particular this demonstrator involves co-designing an early warning system for hailstorms and frost in orchards (peach, persimmons and citrus fruits), doing so jointly with the Valencia Region Agrofood Cooperative Federation (Federación de Cooperativas Agroalimentarias de la Comunidad Valencia: CACV) and the Italian Inter-University Automatic Calculation Consortium (Consorzio interuniversitario per il calcolo automatic: CINECA). This early-warning system would be based on earth observation data (from satellites and in situ sensors), weather forecasts, crop modeling, and advanced data-analysis tools.

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