Europe’s food systems generate vast amounts of data across the entire value chain, yet much of this data remains fragmented or underused due to technical, legal, and trust-related barriers. At the same time, artificial intelligence (AI) and machine learning (ML) offer strong potential to improve sustainability, transparency, efficiency, and healthy dietary choices. FoodDataQuest addresses this challenge by exploring how conventional and unconventional datasets can be responsibly integrated and shared across food value chains, while respecting privacy, ethics, and regulatory requirements.
The project’s overall objective is to develop and validate a trusted framework for AI-enabled data integration in food systems. It combines technical design with social, economic, legal, and ethical analysis to ensure data sharing is technically feasible, socially acceptable, economically viable, and policy-aligned. Social sciences and humanities play a central role by analysing stakeholder needs, trust perceptions, governance models, ethical issues, and behavioural aspects, which are embedded into AI solution design.
FoodDataQuest operationalises this approach through four complementary use cases: (1) a DNA-based, AI-driven system for food sustainability and traceability; (2) data-driven optimisation of food services in hospitals and elderly care homes; (3) sustainability insights for a healthy and climate-neutral food chain; and (4) AI-enabled communication tools to enhance consumer awareness of sustainable choices. These use cases test the framework in real-world contexts and generate actionable insights.
In terms of impact, FoodDataQuest contributes to a key EU priority: integrating AI and data spaces across food value chains. The project delivers a reusable framework and blueprint, while the use cases demonstrate how AI-driven data integration can reduce fraud risks, improve sustainability performance, support healthier diets, and empower stakeholders. Through this combined approach, FoodDataQuest supports Europe’s transition toward more sustainable, transparent, and data-enabled food systems and informs policy, standardisation, and future innovation initiatives.