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CYBELE is integrating tools from high-performance computing, high-performance data analytics, and cloud computing to support the development of more productive, data driven methods for increasing agricultural productivity and reducing food scarcity.

CYBELE creates value and innovation in the high volume business agri-food, which still lacks the required operational efficiency. In particular, it addresses Precision Agriculture and Precision Livestock Farming as demonstrated by nine real world case studies that cover monitoring, feeding optimization, but also crop yield prediction in conjunction with advanced weather simulations. For this purpose, CYBELE leverages the convergence of High Performance Computing, High Performance Data Analytics, and Cloud Computing together with novel paradigms like the Internet of Things or Artificial Intelligence in order to revolutionize farming by reducing scarcity and increasing food supply, but also by bringing social, economic, and environmental benefits.

CYBELE guarantees its stakeholders, which are involved in the entire agri-food value chain, a seamlessly integrated platform with unmediated access to datasets from different domains and access to large-scale High Performance Computing infrastructures that support data discovery, data processing, data analytics, data visualization and with this, value extraction. For this purpose, CYBELE will develop a distributed data management architecture and a related manag

  • interoperable datasets and their metadata of diverse types from a multitude of distributed data sources,
  • data and service driven virtual research environments supporting the efficient execution of multi-parametric agri-food impact models, and
  • a bouquet of domain specific and generic services to manage the virtual research environments that facilitate the strived knowledge elicitation.

Consequently, CYBELE exceeds the traditional boundaries of Precision Agriculture and Precision Livestock Farming by optimizing and enhancing the computational models together with the required data. Fusing climate and weather simulations with satellite-derived earth observation time-series data or sensor information represents only one example for increasing the value of a data set, so that more precise and accurate simulation insights can be obtained as a result.

HLRS builds up the infrastructure services for CYBELE, which involve the development of frontend and backend services to connect the platform components with the High Performance Computing as well as High Performance Data Analytics systems. In addition, HLRS also ports and optimizes the involved modelling and simulation applications and their workloads in order to guarantee a highly efficient application execution.


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