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UC 64 — SmartLarva

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Key Information
Title
AI-driven production-monitoring and phenotyping for insect farming
Acronym
SmartLarva
Coordinator
Aarhus University (Denmark)University
Duration
23 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Sectors
Livestock/animal productionOther
Data Types
Machine sensor dataFarm Management Information Systems (FMIS) dataTest and experimental facilities (TEF research) 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
  • 3.1.2.3 Schemes for data interoperability
  • 3.1.2.4 Reference data sets & non-discriminatory data
  • 3.1.2.7 Multi-layer geospatial data tool with API
  • 3.1.2.8 Context-based curation & curated small data
  • 3.1.2.9 Granularity through smart systems & edge compute
  • 3.1.2.10 Error processing & quantifying methods
  • 3.1.2.11 Models to increase data granularity
Themes
Livestock & animal healthAI / ML / Decision Support
Partners (1)
  • Aarhus UniversityP1
    Coordinator

    DenmarkUniversity

#OrganisationCountryType
P1Aarhus UniversityCoordinatorDenmarkUniversity
Models & Macro data
Statistics Registers data

3.1.3 Data marketplaces and cooperatives in agriculture

  • 3.1.3.1 Service Cloud & network of data-hubs
  • 3.1.3.2 "Pay as you go" system for services
  • 3.1.3.3 Reward mechanisms for data sharing
  • 3.1.3.4 Discoverability & composability of services

3.1.4 Applications of AI techniques

  • 3.1.4.3 Privacy law solutions for satellite imagery
  • 3.1.4.4 AI handling heterogeneous & fuzzy information
  • 3.1.4.5 Data governance for farming data ownership
  • 3.1.4.6 Digital twins of farms & environments

3.2.1 Enhancing functionality of and generating input for DSS including FMIS

  • 3.2.1.4 Take stock of existing FMIS & their uptake
  • 3.2.1.5 Interoperability & switchability for FMIS
  • 3.2.1.6 Multi-criteria simulation modules

3.2.2 Farm modelling systems

  • 3.2.2.1 Take stock of existing modelling approaches
  • 3.2.2.2 Novel forecasting & prediction methodologies
  • 3.2.2.3 Whole-farm & landscape environmental impact

3.2.3 Assessment of farm performance

  • 3.2.3.1 Thematic areas for farm metrics
  • 3.2.3.2 Ambitious farm performance targets
  • 3.2.3.3 Displaying performance & MCDA trade-offs

3.2.4 Data-based solutions for addressing environmental challenges

  • 3.2.4.2 Prescription maps for precision cropping
  • 3.2.4.3 Bridge crop/pasture yield gaps
  • 3.2.4.4 Areas for biodiversity & pollinator conservation
  • 3.2.4.5 Continuous soil & water sensor monitoring

3.2.5 Strategies and technologies for climate change adaptation

  • 3.2.5.2 Lessons from other biogeographic regions
  • 3.2.5.3 High-throughput phenotyping infrastructures

3.3 Data-based solutions for policy-making

  • 3.3.6 Methodologies to monitor compliance
  • 3.3.7 Proposals for future CAP design
  • 3.3.8 Supplement Member States' FaST services
  • 3.3.9 New satellites, drones & ground sensors for policy

4.1 Public-private synergies (R&I activities)

  • 4.1.1.3 Stock-take EU/national R&I projects (umbrella)
  • 4.1.1.4 Moderate innovation ecosystem for umbrella effect
  • 4.1.1.5 Develop reusable data-based solutions

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

  • 4.2.4.5 Highlight Common European Agriculture Data Space
  • 4.2.4.6 Feedback on data-sharing policy instruments
  • 4.2.4.7 Increase trust in agricultural data sharing
  • 4.2.4.8 Frameworks for re-use of publicly-funded data

4.3 Uptake & innovation management (R&I activities)

  • 4.3.1.4 Two-way interactive e-platform & knowledge hub
  • 4.3.1.5 Capacity building & data literacy
  • 4.3.1.6 Training in advanced digital skills (DEP)
  • 4.3.1.7 Persuasive technologies for sustainable practices
  • 4.3.1.8 National mirror groups for policy uptake