USE CASE #2
Research and innovation data for improved AI algorithms performance
- Countries: FR, IT, ES, BE, PL
- Lead Partner: INRAE
- Other Partners: IESE, FBK, UDL, EV ILVO, PSNC, ANAMOB, JR
This Use Case connects CEADS with the AgrifoodTEF Data Space, the upcoming Agriculture of Data partnership, and the EOSC. It ensures that datasets from experiments, robotics, and AI testing are accessible for research and business innovation.
Objectives
Leverage underused research datasets to train and benchmark AI and robotics.
Mutualize technologies (e.g., semantic interoperability) with other programmes.
Support SMEs to validate their solutions against CAP-aligned sustainability criteria.
Technology in Action
Linking testbeds and research infrastructures across EU.
Sharing datasets in controlled environments for algorithm training and benchmarking.
Impacts
Economic
Boosts competitiveness of agribusiness by enabling advanced AI models.
Technological
Helps validate eco-friendly practices as CAP subsidy measures.
Environmental
Helps validate eco-friendly practices as CAP subsidy measures.
Societal
Accelerates AI/robotics adoption, reducing labour burden and creating new services.