Handbook of European HPC projects


Fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics

CYBELE generates innovation and creates value in the domain of agri-food, and its verticals in the subdomains of Precision Agriculture (PA) and Precision Livestock Farm-ing (PLF) in specific. This will be demonstrated by the real-life industrial cases, empowering capacity building within the industrial and research community. CYBELE aims at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the Internet of Things can revolutionise farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits.

CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large-scale datasets of diverse types from a variety of sources. By providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualisation services, Stakeholders shall be enabled to generate more value and deeper insights in operations.
CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing:

  1. integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources,

  2. a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimising the features of processing large scale datasets and

  3. a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.


Project’s email

HPC and Big Data enabled Large-scale Test-beds and Applications


Coordinating Organization:
Waterford Institute of Technology, Ireland

Project Timespan
2019-01-01 – 2022-03-31

Other Partners:
  • BSC – Barcelona Supercomputing Center, Spain
  • Atos (Bull SAS), France
  • CINECA – Consorzio Interuniversitario, Italy
  • Instytut Chemii Bioorganicznej Polskiej Akademii Nauk – Poznań Supercomputing and Networking Center (PSNC), Poland
  • RYAX Technologies, France
  • HLRS – High Performance Computing Center – Universität Stuttgart, Germany
  • EXODUS Anonymos Etaireia Pliroforikis, Greece
  • LEANXCALE SL, Spain 
  • ICCS – Institute of Communication and Computer Systems, Greece
  • FORTH – Foundation for Research and Technology – Hellas, Greece
  • Tampereen Korkeakoulusaatio SR, Finland
  • Ubitech, Greece
  • University of Piraeus Research Center, Greece
  • SUITE5 Data Intelligence Solutions Ltd, Cyprus
  • INTRASOFT International SA, Luxembourg
  • ENGINEERING – Ingegneria Informatica SPA, Italy
  • Wageningen University, Netherlands
  • Stichting Wageningen Research, Netherlands
  • BIOSENSE Institute, Serbia
  • Donau Soja Gemeinnutzige GmbH, Austria
  • Agroknow IKE, Greece
  • GMV Aerospace and Defence SA, Spain
  • Federacion de Cooperativas Agroalimenta-res de la Comunidad Valenciana, Spain
  • University of Strathclyde, United Kingdom
  • ILVO – Instituut voor Landbouw- en Visserrijonderzoek, Belgium
  • VION Food Nederland BV, Netherlands
  • Olokliromena Pliroforiaka Sistimataae, Greece
  • Kobenhavns Universitet, Denmark
  • EVENFLOW, Belgium
  • Open Geospatial Consortium (Europe) Ltd, United Kingdom