top of page

Project Acronym: CYBELE

Project Full Title: Fostering Precision Agriculture and Livestock Farming through Secure Access to Large-Scale HPC-enabled Virtual Industrial Experimentation Environment empowering Scalable Big Data Analytics

Duration: 01/01/2019 – 31/03/2022

Topic: ICT-11-2018-2019 - HPC and Big Data enabled Large-scale Test-beds and Applications

Project Website:

Suite5 in CYBELE

Suite5 leads the activities related to data check-in, data provenance and data management, as well as the design and implementation of the Data Policy & Assets Brokerage Framework.

CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of Precision Agriculture (PA) and Precision Livestock Farming (PLF) in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of High-Performance Computing (HPC), Big Data, Cloud Computing and the IoT can revolutionize 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, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. 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, optimizing 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.

Other resources:

bottom of page