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UC 17 — AI-FFS

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Key Information
Title
AI for Future Food Systems
Acronym
AI-FFS
Coordinator
Wageningen Research (Netherlands)Research Institute
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Research Data Infrastructures, Benchmark Datasets & Methods
Sectors
Arable cropFruitsVegetables
Data Types
Farm Management Information Systems (FMIS) dataTest and experimental facilities (TEF research) dataStatistics Registers data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.3 Schemes for data interoperability
  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.10 Error processing & quantifying methods

3.1.4 Applications of AI techniques

  • 3.1.4.1 Identify key reference/training data sets
  • 3.1.4.2 Capitalize historical satellite data
  • 3.1.4.7 Strengthen AI uptake; trust in AI

3.2.1 Enhancing functionality of and generating input for DSS including FMIS

  • 3.2.1.6 Multi-criteria simulation modules

3.2.3 Assessment of farm performance

  • 3.2.3.1 Thematic areas for farm metrics
Themes
AI / ML / Decision Support
Partners (2)
  • Wageningen ResearchP1
    Coordinator

    NetherlandsResearch Institute

  • Wageningen ResearchP2

    NetherlandsResearch Institute

#OrganisationCountryType
P1Wageningen ResearchCoordinator

Short summary of your UC This use case explores how generative AI can reshape the way research on agri-food systems is conducted. Rather than focusing on a single application, it brings together a set of interconnected use cases that experiment with AI across research data, models and knowledge. Introduction Research on food systems is becoming increasingly data-intensive and complex, while much knowledge remains fragmented across reports, datasets and models. At the same time, generative AI is rapidly evolving, but its implications for scientific research processes are not yet well understood. Methodologies The work combines literature-based analysis with hands-on experimentation in five use cases. These include AI-enabled market outlook models, local language advisory services, knowledge extraction using knowledge graphs, personalized nutrition modelling and commodity market analysis. Different techniques are applied, such as retrieval-augmented generation, processing of unstructured data, and integration of knowledge graphs into AI systems. Output The use case results in working prototypes, comparative insights across use cases, and scientific outputs that reflect on how generative AI changes research workflows. Impact The main impact lies in improving the speed and quality of research, while also identifying limitations, risks and failure modes of generative AI in scientific contexts. This contributes to more responsible and effective use of AI in food systems research. What I can offer Experience with multiple applied AI use cases, access to a broad research network in agri-food, and practical methods to test and evaluate generative AI in research settings. What I need Collaboration with partners working on similar challenges, especially those deploying large language models in their own infrastructure, to exchange approaches and validate findings.

Source: Annual AgData Use Case Summit 2026 booklet.

Livestock/animal production
Dairy
  • 3.2.3.2 Ambitious farm performance targets

    • 3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.1 Assess needs for environmental decision support
    • 3.2.4.5 Continuous soil & water sensor monitoring

    3.2.5 Strategies and technologies for climate change adaptation

    • 3.2.5.3 High-throughput phenotyping infrastructures

    4.2 Data governance, standards and security (R&I activities)

    • 4.2.4.3 Harmonised access to public-sector data for research

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.1 Evidence of the value of data technologies
    • 4.3.1.6 Training in advanced digital skills (DEP)
    Netherlands
    Research Institute
    P2Wageningen ResearchNetherlandsResearch Institute