Distributed Sensing in Horticultural Environments

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George Kantor

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

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