Boosting data availability and AI solutions in food for consumers and food service professionals

Expected Outcome

Project results are expected to contribute to all of the following expected outcomes:

• generative AI solutions for consumers and food service professionals have significantly scaled-up and improved the productivity and sustainability (including for mitigation of climate change) of food purchasing, preservation and preparation, and enabled dietary health;
• high value and high quality digital datasets for food products, and data driven consumer and food service applications are developed and contribute to increasing productivity, sustainability and dietary health;
• data sovereignty is ensured, including for consumers and farmers.

Scope

The decisions taken by consumers and food service professionals when they purchase, prepare and preserve food impact their productivity as well as the sustainability of food systems, and dietary health. AI solutions offer opportunities to assist consumers and food service professionals in these decisions. To maximise the impact of these AI solutions it is necessary to increase their uptake as well as the availability and quality of the data on which they rely.

Proposals should:

• develop and deploy human-centric, generative AI solutions for consumers and food service professionals to support them in the processes of food purchasing, preservation and preparation, with a view to increase dietary health, sustainability (lower greenhouse gas emissions, energy & water footprint; lower food waste, and packaging waste) and productivity.
• support innovators working on generative AI solutions in food. The support should include: Encourage participation of SMEs and start-ups, such as through high-impact innovation platforms (e.g. EU Digital Innovation Hubs, accelerators, incubators). Offer innovation support packages that are tailored to innovative SMEs and start-ups and that are effective in bringing solutions to market. Prioritize support based on end-users needs, data availability and the likely impact potential on dietary health, sustainability and productivity when solutions are scaled-up, including for reducing greenhouse gas emissions;
• engage with a broad range of stakeholders (e.g. consumers, retailers, caterers, equipment manufacturers) to ensure that the solutions developed are practical and widely adopted. Include plans for providing training and ongoing support to end users to maximize the effective and significant use of the AI solutions;
• advance the access to trustworthy digital product data in food. Improve the availability, sovereignty and quality of data sets that are high value in view of end-users needs, and the likely impact potential on dietary health, sustainability and productivity when solutions are scaled-up, including for reducing greenhouse gas emissions;
• encourage businesses and other relevant actors (e.g. public authorities, certification bodies) in the food system to enhance data capture, optimize data sharing, and promote data reuse;
• ensure that sufficient and good quality data is mobilised from different relevant data sources (e.g. in-situ data, model data, sensor data, device data, remote sensing, personal data, government data) across the computing continuum, and from different parts of the food system, from primary production, over food processing to consumption and disposal.

Where appropriate, proposals should use, federate, optimise and complete tools, capacities and infrastructures created under the Digital Europe Program (incl. European Data Spaces, EDIHs) and under Horizon Europe projects and partnerships (incl. Partnerships Agriculture of Data & FutureFoods). Proposals are encouraged to building on results from previous and relevant calls and projects. Proposals should also comply with existing EU framework and strategies and building upon the concepts and solutions developed in other Union initiatives aimed to facilitate data sharing, such as the Common European Agricultural Data Space (CEADS).

Proposals must implement the 'multi-actor approach’ and ensure adequate involvement of the main stakeholders of food systems and the data economy. This topic should involve the effective contribution of Social Sciences and Humanities (SSH) disciplines.