Fostering Precision Agriculture and Livestock Farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable Big Data analytics (CYBELE)
CYBELE is a 3-year project to demonstrate how high performance computing (HPC) and Big Data analysis can help revolutionize agriculture and boost precision farming in order to create social, economic and environmental benefits. CYBELE aims to ensure that the stakeholders have a secure access to large-scale datasets and have the processing power available to analyse and process this data, generate value and extract useful insights. The project is 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.
After an analysis and design phase, the CYBELE integrated platform is developed, including all the necessary mechanisms, tools, services and algorithms to work with the HPC infrastructure. Nine demonstrators in the precision agriculture and precision livestock farming fields have been selected to showcase and evaluate the potential of the HPC infrastructure. ILVO is WP leader of the precision livestock farming demonstrators and is also involved in the work of two of these demonstrators. One on sustainable pig production, together with Vion Food Group, focuses on a better usage of the vast amounts of data collected in a slaughterhouse, hyperspectral imaging for meat quality and the usage of sensor data to alert for pig diseases on-farm. The other demonstrator, on open sea fishing, focuses on better monitoring of fish prevalence and the marine ecosystem of the North Sea, improvement of fisheries management via multiple sensors on the ship, and the automatic detection of fish species through image analysis.
CYBELE has achieved milestones in several areas: (a) an HPC environment that supports the execution of agrifood-related experiments to optimize the processing of large-scale datasets; (b) several cases in precision agriculture and precision farming that test, use and improve the HPC environment through iterative feedback. Overall, this project leads to a more accessible HPC environment with features adapted to the agri-food sector, to more advanced analyses for scientists and policy makers, to faster response times and improved decisions for companies and parties in the chain, and to the unlocking and more efficient use of available data in the chain. The solutions themselves being built in this environment lead, among others, to more responsible use of natural resources, improved farm management, reduced waste and losses, etc.