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Distributed Sensing in Horticultural Environments George Kantor Carnegie Mellon University International Horticultural Congress Lisboa 2010 Colloquium 6: Technical Innovation in Horticulture 25 August 2010
Sensor Networks for Agriculture ,[object Object]
ad-hoc network
data collected, relayed back to central point
can also send control signalsSensors (leaf wetness, temperature, humidity, etc.) node field Internet base station G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
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
Visualizing Spatial Variation G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010
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
Technology Overview: Robot G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
Laser Scanning G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
Building Point Clouds G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
point cloud created by  Ben Grocholsky G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
Technology Overview: Robot NDVI cameras G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
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
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
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
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
Information is Worthless… G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
Information is Worthless… …unless you use it to do something! G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
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
Ongoing Work: Experimental Setup G. Kantor CMU Robotics Institute IHC 2010Lisboa 25 August 2010 Sensing for Horticulture
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
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
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
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
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
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

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Distributed Sensing in Horticultural Environments

  • 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.
  • 4. data collected, relayed back to central point
  • 5. can also send control signalsSensors (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