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UC 61 — SmartField

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Key Information
Title
SmartField
Acronym
SmartField
Coordinator
University of Copenhagen (Denmark)University
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Sectors
VegetablesOther
Data Types
Earth observation 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
  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.6 Boost data re-usability through quality control
  • 3.1.2.7 Multi-layer geospatial data tool with API
Themes
Crop production & monitoring
Partners (1)
  • University of CopenhagenP1
    Coordinator

    DenmarkUniversity

#OrganisationCountryType
P1University of CopenhagenCoordinatorDenmarkUniversity
Statistics Registers data
Other
  • 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
  • 3.1.2.12 Solutions for private/public interests
  • 3.1.2.13 Procedures to aggregate sensitive data
  • 3.1.2.14 Monitor functionality & product evolution

    • 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
    • 3.1.3.5 User-adapted data payment services

    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
    • 3.1.4.3 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
    • 3.1.4.7 Strengthen AI uptake; trust in AI

    3.2.1 Enhancing functionality of and generating input for DSS including FMIS

    • 3.2.1.1 Data layers & algorithms for FMIS services
    • 3.2.1.2 Extrapolate farm-generated sensor data
    • 3.2.1.3 New satellite imagery & ground sensors for DSS
    • 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
    • 3.2.1.7 Increase profit of DSS/FMIS use
    • 3.2.1.8 Business models demonstrating ROI

    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
    • 3.2.2.4 Farm modelling for optimal practice
    • 3.2.2.5 Farm modelling for agri-environmental measures

    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
    • 3.2.3.4 Obstacles in on-farm data collection
    • 3.2.3.5 Long-term funding strategy for indicators

    3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.1 Assess needs for environmental decision support
    • 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
    • 3.2.4.6 Robots/UGV + IoT for pest & weed detection
    • 3.2.4.7 Long-term experiments for soil health & C

    3.2.5 Strategies and technologies for climate change adaptation

    • 3.2.5.1 Resilient livestock & cropping systems
    • 3.2.5.2 Lessons from other biogeographic regions
    • 3.2.5.3 High-throughput phenotyping infrastructures
    • 3.2.5.4 Transformational DSS for resilient agriculture

    3.3 Data-based solutions for policy-making

    • 3.3.1 Identify data needs for policy monitoring
    • 3.3.2 Take stock of existing indicators & approaches
    • 3.3.3 Common-approach indicators across MS
    • 3.3.4 Monitor agri-environmental conditions & GAEC
    • 3.3.5 Extend Area Monitoring System (AMS)
    • 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
    • 3.3.10 Europe-wide upscaling of (precision) farming data

    4.1 Public-private synergies (R&I activities)

    • 4.1.1.1 Establish governance structures

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

    • 4.2.4.2 Privacy-preserving handling of personal data
    • 4.2.4.4 Legal interpretation services

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.1 Evidence of the value of data technologies
    • 4.3.1.2 Communicate value to end-users
    • 4.3.1.3 User-friendly data platforms
    • 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
    • 4.3.1.9 Connect actors for climate-adaptation innovation
    • 4.3.1.10 Business & governance models for data flows
    • 4.3.1.11 Promote open science
    • 4.3.1.12 Communication, brokerage & events