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UC 43 — AI-DSS

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Key Information
Title
Artificial Intelligence based Decision Support Systems (AI-DSS) for sustainable agriculture
Acronym
AI-DSS
Coordinator
Jožef Stefan Institute (Slovenia)Research Institute
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Decision Support Systems & FMIS Integration
Sectors
Arable cropFruitsVegetables
Data Types
Machine sensor dataEarth observation dataFarm Management Information Systems (FMIS) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.1 Data acquisition & re-use framework
  • 3.1.2.2 Link databases & computing capacities
  • 3.1.2.4 Reference data sets & non-discriminatory data

3.1.4 Applications of AI techniques

  • 3.1.4.4 AI handling heterogeneous & fuzzy information
  • 3.1.4.7 Strengthen AI uptake; trust in AI

3.2.1 Enhancing functionality of and generating input for DSS including FMIS

  • 3.2.1.5 Interoperability & switchability for FMIS
  • 3.2.1.6 Multi-criteria simulation modules

3.2.2 Farm modelling systems

  • 3.2.2.3 Whole-farm & landscape environmental impact
Themes
AI / ML / Decision Support
Partners (1)
  • Jožef Stefan InstituteP1
    Coordinator

    SloveniaResearch Institute

#OrganisationCountryType
P1Jožef Stefan InstituteCoordinatorSloveniaResearch Institute

Introduction AI-DSS develops a methodology for creating trustworthy AI-powered Decision Support Systems (DSS) for sustainable agriculture. Using soil health assessment and management as a demonstration case, the project combines data, scientific knowledge, and expert knowledge to support improved agricultural decision-making. Methodologies The project integrates sensor and measured data, Earth Observation data, FMIS data, scientific literature, and expert knowledge using data mining, machine learning, knowledge graphs, explainable AI, and Large Language Models (LLMs). User-centred design and stakeholder personas ensure that the solutions meet the needs of farmers, advisers, researchers, and policymakers. Output A prototype AI-DSS for soil health assessment and management, along with reusable methods for knowledge integration, explainable AI, knowledge graphs, and stakeholder-oriented decision support system design, applicable across various agricultural domains. Impact AI-DSS aims to increase trust, transparency, and adoption of AI solutions in agriculture. The project contributes to more sustainable land management and supports evidence-based decision-making aligned with European sustainability objectives. What I can offer? • Expertise in AI-based decision support systems (DSS) • Experience with explainable AI and machine learning methods • Knowledge graphs and semantic technologies • Proficiency in multi-criteria decision modelling • Expertise in soil health assessment and management • Collaboration on AI, DSS, and data integration challenges What I need? • Agricultural datasets and real-world use cases for validation • Partners interested in testing AI-DSS methodologies • Collaboration on data interoperability and knowledge integration • Connections with FMIS providers, advisors, and end users

Source: Annual AgData Use Case Summit 2026 booklet.

Livestock/animal production
Dairy
Test and experimental facilities (TEF research) data
Models & Macro data
Statistics Registers data
  • 3.2.2.4 Farm modelling for optimal practice
  • 3.2.2.5 Farm modelling for agri-environmental measures

    • 3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.1 Assess needs for environmental decision support
    • 3.2.4.3 Bridge crop/pasture yield gaps
    • 3.2.4.5 Continuous soil & water sensor monitoring

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.1 Evidence of the value of data technologies
    • 4.3.1.3 User-friendly data platforms
    • 4.3.1.5 Capacity building & data literacy