The SCRI-MINDS Project
Wireless Sensor Networks for
Decision Irrigation Management
George Kantor 1 and John Lea-Cox 2
1
Robotics Institute
Carnegie Mellon University
2
Dept. of Plant Science and Landscape Architecture
University of Maryland
[email protected]
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Funding provided by USDA-NIFA-SCRI Award no. 2009-51181-05768
Presentation Outline:
1.
What are the Issues?
2.
Hardware and Software Development
3.
Grower Partner Implementation, Results
4.
Economic Impact, ROI
5.
Challenges
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Why Sensor Networks?

Because very few growers are monitoring practices

Water management is the key to nutrient management
and optimizing growth

We need to move from precision irrigation to
precision + decision irrigation management

Growers typically won’t change practice unless you
convince them that it will improve productivity or
profitability
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Water Management is the Key to Many Issues
Plant Yield
Water and Nutrient Uptake
Irrigation Water
Application
Fertilizer
pathogens / water
Nutrient
leaching
Surface runoff
Substrate
components
Courtesy of Dr. Jim Owen,
Virginia Tech University
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Sensor
NetworksTeams and Working Groups
SCRI-MINDS:
University of Maryland:
• John Lea-Cox (PM, Micro-scale
Modeling – Nursery, Extension Outreach)
Andrew Ristvey, Steven Cohan (Green Roof)
• Erik Lichtenberg (Economics – Private Benefits)
•
UM-Center Environmental Science:
• Dennis King (Socio Economics – Public Benefits)
Colorado State:
• Bill Bauerle, Mike Lefsky, Stephanie Kampf
Carnegie Mellon:
• George Kantor, David Kohanbash
(Next Generation Core Software
Development)
Decagon Devices:
• Todd Martin, Colin Campbell
Lauren Bissey
(Next Generation Hardware
Development, Core Software)
(LIDAR, Hydrology, Macroscale modeling – Nursery)
University of Georgia:
• Marc van Iersel, Paul Thomas, John Ruter,
Matthew Chappell (Microscale modeling –
Greenhouse, Extension and Outreach)
Cornell:
• Taryn Bauerle (Microscale – Root Environments)
Smart-Farms.net
— Managing Irrigation and Nutrition via Distributed Sensing
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Antir Software:
• Richard Bauer
(Crop Modeling Software,
Core Software )
Successful Implementation:

Technology needs to be cost-effective (short ROI)

Efficient; Information must be easy to interpret
(5-minute decision window)

Sensors need to be accurate (plug ‘n play)

Systems must be robust (low maintenance)
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
New nR5-Control Node: (Field tested during 2012)
nR5-DC Node
 nR5 Node – This wireless node
allows us to both monitor sensors
and control irrigation events, based
on sensor readings
 Node measures data every minute
and then logs the data at an interval
specified by the user (1, 2, 5, 15, 30,
60 minutes etc.)
 Monitoring Mode: Batteries logging
at 15 minutes typically last 12+
months
 Control mode: Batteries are lasting
4-6 months, depending on the #
irrigations initiated per day.
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Sensorweb Software: Enables remote and/or automatic control of
irrigation schedules, via a customized web-based interface
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Sensorweb: Macro-Scheduling Tool
Allows for real-time monitoring and adjustment of irrigation events, for blocks
of times during the day, using sensor-based or schedule-based control
Maple Block
Dogwood Block
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Sensorweb: Micro-pulse Tool
Allows a “time-out” for sensors to measure between pulse events, reducing
leaching fractions (and nutrient loss) to minimal amounts
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Sensorweb: Local Set-Point Irrigation
Augments Scheduling and Micro-pulse tools with soil moisture sensor
feedback: irrigation is disabled with set-point is exceeded
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
12V Solenoid and Flow meter Integration
nR5-DC Node
 nR5-DC Node – Integrated with a flow meter
and controls a 12V solenoid valve
 Allows us to control and measure water
applications in remote fields where there is
NO electrical power
12V DC latching
solenoid + 1” valve
Flow meter
Bleed valve
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Current BMP: Timed Cyclic Irrigation
Monitoring Block: 3 to 4 timed cyclic irrigation events per day
4 x 6-minute Irrigation Events = 164 Gals / day
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Future Management Practice?
Control Block: Reduced (2-min) pulse events PLUS a reduction in
number of total irrigation events (based on substrate water content)
1-2 Irrigations per day @ 21 gal / 2 min pulse
2-minute Irrigation Events
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Dogwood Monitoring vs. Control
Water Use: April – October, 2012
Irrigation
Method
Grower: Timed,
Cyclic
Sensor: Setpoint
Control
Total Water Use
(Gals / Row)
Average Water
Application
(Gals/ Tree /Day)
28,334
0.922
10,521
0.342
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Av. Efficiency
(Timed vs.
Control)
Water Savings
(Control vs.
Timed)
0.371
2.69
Saving Water in Three Ways: Supply and Demand
1. Micropulse: Smarter delivery, through reduced irrigation duration
Outcome: Oftentimes, only a single 2-minute pulse is required
2. Environment: Sensing plant water demand, as a function of
changes in environmental conditions
Outcome: Reduction in total number of irrigations per day, based
on evapotranspiration demand, (light, temp/RH and rainfall)
3. Growth: Sensing plant water demand, as a function of plant
growth
Outcome: Irrigation scheduling is based on plant growth
(increased or decreased demand), optimizing root water status
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Implementation, Return on Investment
McCorkles Nursery (GA)
 14-month production cycle
collapsed to 8-month
 30% loss to Disease reduced
to virtually zero
 > 100 Million gal reduction
in total water use / year
 Economic Gain = $1.06 / ft2
(total net revenue = $20,700
for crop)
 ROI < 3 months for $6,000
network
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Implementation, Return on Investment
Hale and Hines Nursery (TN)
 Sensor-controlled Irrigation
reduced water use by 63%
= $$$ in labor (TBD)
 Growth of trees was equivalent
 > 43 Million gal reduction
in total water use / year;
$6000 in pumping costs
 Translated to CA, net savings in
water cost (@ $750/ acre ft) =
$100K per year
 ROI < 3 months for $25K network
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Challenges
 Scaling networks to large acreages
 Nurseries are site-specific in
their needs (problems)
 Many species with specific
needs (will probably require a
“indicator species” approach)
Support Issues (Consultant network)
for specific regions (environments)
and specific crops
Apply to other specialty crops: need
partnerships with domain specialists
and growers.
 It’s a big country!
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Project Information at http://smart-farms.net
Funding provided by USDA-NIFA-SCRI Award no. 2009-51181-05768
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing