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
Funding provided by USDA-NIFA-SCRI Award no. 2009-51181-05768
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
1. What are the Issues?
2. Hardware and Software Development
3. Grower Partner Implementation, Results
4. Economic Impact, ROI
5. Challenges
Presentation Outline:
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Because very few growers are monitoring practices
Why Sensor Networks?
Water management is the key to nutrient management and optimizing growth
Growers typically won’t change practice unless you convince them that it will improve productivity or profitability
We need to move from precision irrigation to precision + decision irrigation management
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Fertilizer
Surface runoff
Substrate
components
Water and Nutrient Uptake
Nutrient
leaching
pathogens / water
Irrigation Water
Application
Plant Yield
Water Management is the Key to Many Issues
Courtesy of Dr. Jim Owen, Virginia Tech University
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
University of Maryland:
• John Lea-Cox (PM, Micro-scale
Modeling – Nursery, Extension Outreach)
• Andrew Ristvey, Steven Cohan (Green Roof)
• Erik Lichtenberg (Economics – Private Benefits)
Carnegie Mellon:
• George Kantor, David Kohanbash
(Next Generation Core Software Development)
Cornell:
• Taryn Bauerle (Microscale – Root Environments)
UM-Center Environmental Science:
• Dennis King (Socio Economics – Public Benefits)
Decagon Devices: • Todd Martin, Colin Campbell Lauren Bissey
(Next Generation Hardware Development, Core Software)
Antir Software:
• Richard Bauer
(Crop Modeling Software,
Core Software )
Colorado State:
• Bill Bauerle, Mike Lefsky, Stephanie Kampf
(LIDAR, Hydrology, Macroscale modeling – Nursery)
University of Georgia:
• Marc van Iersel, Paul Thomas, John Ruter, Matthew Chappell (Microscale modeling – Greenhouse, Extension and Outreach)
SCRI-MINDS: Teams and Working Groups Sensor Networks
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Efficient; Information must be easy to interpret
(5-minute decision window)
Systems must be robust (low maintenance)
Technology needs to be cost-effective (short ROI)
Successful Implementation:
Sensors need to be accurate (plug ‘n play)
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.
New nR5-Control Node: (Field tested during 2012)
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
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
Sensorweb: Macro-Scheduling Tool
Maple Block
Dogwood Block
Allows for real-time monitoring and adjustment of irrigation events, for blocks of times during the day, using sensor-based or schedule-based control
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Allows a “time-out” for sensors to measure between pulse events, reducing leaching fractions (and nutrient loss) to minimal amounts
Sensorweb: Micro-pulse Tool
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Augments Scheduling and Micro-pulse tools with soil moisture sensor feedback: irrigation is disabled with set-point is exceeded
Sensorweb: Local Set-Point Irrigation
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
12V DC latching solenoid + 1” valve
Bleed valve
Flow meter
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 Solenoid and Flow meter Integration
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Current BMP: Timed Cyclic Irrigation
4 x 6-minute Irrigation Events = 164 Gals / day
Monitoring Block: 3 to 4 timed cyclic irrigation events per day
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Future Management Practice?
2-minute Irrigation Events
1-2 Irrigations per day @ 21 gal / 2 min pulse
Control Block: Reduced (2-min) pulse events PLUS a reduction in number of total irrigation events (based on substrate water content)
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
Dogwood Monitoring vs. Control
Water Use: April – October, 2012
Irrigation
Method
Total Water Use
(Gals / Row)
Average Water
Application
(Gals/ Tree /Day)
Av. Efficiency
(Timed vs.
Control)
Water Savings
(Control vs.
Timed)
Grower: Timed,
Cyclic 28,334 0.922
0.371 2.69 Sensor: Setpoint
Control 10,521 0.342
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
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