The SCRI-MINDS Project Wireless Sensor Networks for Decision Irrigation Management

Transcription

1 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 JLC@umd.edu Funding provided by USDA-NIFA-SCRI Award no

2

3 Presentation Outline: 1. What are the Issues? 2. Hardware and Software Development 3. Grower Partner Implementation, Results 4. Economic Impact, ROI 5. Challenges

4 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

5 Water Management is the Key to Many Issues Irrigation Water Application Plant Yield Water and Nutrient Uptake Fertilizer pathogens / water Surface runoff Nutrient leaching Substrate components Courtesy of Dr. Jim Owen, Virginia Tech University

6 Sensor SCRI-MINDS: Networks Teams and Working Groups 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 (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) 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) Antir Software: Richard Bauer (Crop Modeling Software, Core Software )

7 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)

8 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.

9 Sensorweb Software: Enables remote and/or automatic control of irrigation schedules, via a customized web-based interface

10 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

11 Sensorweb: Micro-pulse Tool Allows a time-out for sensors to measure between pulse events, reducing leaching fractions (and nutrient loss) to minimal amounts

12 Sensorweb: Local Set-Point Irrigation Augments Scheduling and Micro-pulse tools with soil moisture sensor feedback: irrigation is disabled with set-point is exceeded

13 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 Flow meter 12V DC latching solenoid + 1 valve Bleed valve

14 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

15 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 21 gal / 2 min pulse 2-minute Irrigation Events

16 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 Sensor: Setpoint Control 28, ,

17 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

18 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 / ft 2 (total net revenue = $20,700 for crop) ROI < 3 months for $6,000 network

19 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

20 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!

21 Project Information at Funding provided by USDA-NIFA-SCRI Award no