Distributed Sensing in Horticultural Environments

George KantorCarnegie Mellon University

International Horticultural Congress Lisboa 2010Colloquium 6: Technical Innovation in Horticulture

25 August 2010

Sensor Networks for Agriculture

basestation

node

Sensors(leaf wetness, temperature, humidity, etc.)

field

Internet

• self-contained “nodes” (radio+IO)• ad-hoc network• data collected, relayed back to central

point• can also send control signals

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Visualizing Time Series(PSU FREC, ZedX Inc.)

FREC Building

North

50m

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Visualizing Spatial Variation

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics Institute

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???

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Technology Overview: Robot

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Laser Scanning

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Building Point Clouds

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

point cloud created by Ben Grocholsky

Technology Overview: Robot

cameras

NDVI

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing 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,…

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing 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

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing 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

x

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing 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

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Information is Worthless…

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Information is Worthless…

…unless you use it to do something!

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Set Point Irrigation

high setpoint

low setpoint

soil moisture measurement

irrigation events

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Ongoing Work: Experimental Setup

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Human in the Loop

basestation irrigation

scheduler

John Lea-Cox Charles Bauers

soil moisture sensors at 12 locations

38% increase in #1 stems

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Automatic Decision Making: Modeling Approach

ModelMapping to

Control Decision

Model Parameters

sensorinputs

modeloutputs control

signal

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Example: Petunia Model [van Iersel et al.]

Model

Mapping to Control Decision (replace

amount of water used)

Model Parameters

varietyplant age

sensor inputs:temperatureRHlight

model output:water use irrigation

command

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Feedforward Modification

Model(with

parameters)

replace difference

sensor inputs:temperatureRHlight

water useirrigationcommand

Model(with

parameters)

set daily irrigation schedule

weather forecast:temperatureRHlight

predictedwater use irrigation

schedule

At the beginning of each day:

At the end of each day:

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Example: MAESTRA [e.g., Bauerle et al.]

Model

Mapping to Control Decision (replace

amount of water used)

Model Parameters

tree location, geometry, s

oil type, LAI, leaf physiology…

sensor inputs:temperatureRHPARwind

model output:water use irrigation

command

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

Example: MAESTRA [e.g., Bauerle et al.]

Model

Mapping to Control Decision (replace

amount of water used)

Model Parameterstree location,

geometry, soil type, LAI, leaf physiology…

sensor inputs:temperatureRHPARwind

model output:water use irrigation

command

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

IHC 2010Lisboa25 August 2010

G. KantorCMU Robotics Institute

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 OrganizersIHC 2010Lisboa25 August 2010

G. KantorCMU Robotics InstituteSensing for Horticulture

O Fim