Distributed Sensing in Horticultural Environments

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George Kantor

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  • 1. Distributed Sensing in Horticultural Environments
    George Kantor
    Carnegie Mellon University
    International Horticultural Congress Lisboa 2010
    Colloquium 6: Technical Innovation in Horticulture
    25 August 2010
  • 2. Sensor Networks for Agriculture
    • self-contained “nodes” (radio+IO)
    • 3. ad-hoc network
    • 4. data collected, relayed back to central point
    • 5. can also send control signals
    Sensors
    (leaf wetness, temperature, humidity, etc.)
    node
    field
    Internet
    base
    station
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 6. G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 7. Visualizing Time Series(PSU FREC, ZedX Inc.)
    FREC Building
    University
    Drive
    North
    50m
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 8. Visualizing Spatial Variation
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
  • 9. Sensor Net Sensor Requirements
    Hands off operation
    No/little calibration required
    Extremely rugged
    Inexpensive
    Generate small amounts of data
    Require low computational power
    Require low electrical power
    Examples:
    today: temperature, RH, PAR, light, rain, soil moisture, soil EC, leaf wetness, wind speed/direction, etc.
    future: stem water potential, fruit temperature, fruit size, sap flow, others???
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 10. Technology Overview: Robot
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 11. G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 12. Laser Scanning
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 13. Building Point Clouds
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 14. point cloud created by
    Ben Grocholsky
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 15. Technology Overview: Robot
    NDVI
    cameras
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 16. Robot Sensing Requirements
    Hands off operation
    Can have non-trivial calibration step
    Moderately rugged
    Can be expensive
    Can generate large amounts of data
    Can require large computing power
    Can require large electrical power
    Examples:
    today: laser scanners, cameras, hyperspectral imagery
    future: gas exchange, chlorophyll, pheromone, leaf area,…
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 17. Robots vs. Sensor Nets
    High spatial resolution
    Low temporal resolution
    Sophisticated sensing
    More Expensive
    Moderate spatial resolution
    High temporal resolution
    Simple sensing
    Less expensive
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 18. Robots vs. Sensor Nets
    x
    High spatial resolution
    Low temporal resolution
    Sophisticated sensing
    More Expensive
    Moderate spatial resolution
    High temporal resolution
    Simple sensing
    Less expensive
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 19. Robots living together in harmony with Sensor Nets
    High spatial resolution
    Low temporal resolution
    Sophisticated sensing
    More Expensive
    Moderate spatial resolution
    High temporal resolution
    Simple sensing
    Less expensive
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 20. Information is Worthless…
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 21. Information is Worthless…
    …unless you use it to do something!
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 22. Set Point Irrigation
    low setpoint
    high setpoint
    soil moisture measurement
    irrigation events
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 23. Ongoing Work: Experimental Setup
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 24. Human in the Loop
    soil moisture sensors at 12 locations
    38% increase in #1 stems
    Charles Bauers
    John Lea-Cox
    base
    station
    irrigation
    scheduler
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 25. Automatic Decision Making: Modeling Approach
    Model Parameters
    model
    outputs
    Model
    Mapping to Control Decision
    sensor
    inputs
    control
    signal
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 26. Example: Petunia Model [van Iersel et al.]
    Model Parameters
    variety
    plant age
    Mapping to Control Decision (replace amount of water used)
    model
    output:
    water use
    sensor inputs:
    temperature
    RH
    light
    Model
    irrigation
    command
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 27. Feedforward Modification
    At the beginning of each day:
    weather forecast:
    temperature
    RH
    light
    predicted
    water use
    set daily irrigation schedule
    Model
    (with parameters)
    irrigation
    schedule
    At the end of each day:
    sensor inputs:
    temperature
    RH
    light
    Model
    (with parameters)
    replace difference
    water use
    irrigation
    command
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 28. Example: MAESTRA [e.g., Bauerle et al.]
    Model Parameters
    tree location, geometry, soil type, LAI, leaf physiology…
    Mapping to Control Decision (replace amount of water used)
    model
    output:
    water use
    sensor inputs:
    temperature
    RH
    PAR
    wind
    Model
    irrigation
    command
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 29. Example: MAESTRA [e.g., Bauerle et al.]
    Model Parameters
    tree location, geometry, soil type, LAI, leaf physiology…
    Mapping to Control Decision (replace amount of water used)
    model
    output:
    water use
    sensor inputs:
    temperature
    RH
    PAR
    wind
    Model
    irrigation
    command
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 30. G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
  • 31. Obrigado
    USDA SCRI CASC Project: CMU, Penn State, Washington State, Purdue, Oregon State, Vision Robotics
    USDA SCRI MINDS Project: U. Maryland, CMU, Georgia, Colorado State, Cornell, Decagon Devices, Antir Software
    Jim McFerson and WTFRC
    IHC 2010 Organizers
    G. Kantor
    CMU Robotics Institute
    IHC 2010Lisboa
    25 August 2010
    Sensing for Horticulture
  • 32. O Fim