Dr. John Lea-Cox
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Transcript of Dr. John Lea-Cox
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John D. Lea‐Cox, PhDSystematic Sensing, LLC
Using Sensor Networks for Precision Water and Nutrient Management in Controlled Environments
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Overview
Sensor Networks: A Toolbox
The Hardware and Software
Sensor‐based Irrigation Control
Benefits, Return on Investment
Alerts and Fault Detection
Environmental Monitoring – Crop Development, Predictive IPM and Disease Management
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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 CampbellLauren 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 GroupsSensor Networks
Smart-Farms.net — Managing Irrigation and Nutrition via Distributed Sensing
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Smart Toolbox
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PlantPoint™ Irrigation Control Hardware
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Various soil moisture sensorsSoil EC, moisture and
temperature
Soil Moisture, EC and Irrigation Sensors
In line Water Pressure In‐line, Tank EC Flow Meters
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Photosynthetic and Total radiation
Leaf wetness, Dew and Ice
Precipitation
temperature, RHand VPD
Wind speed and direction
Sonic anemometer
Environmental Sensors
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SensorWeb™ Software Interface
Operation’s Homepage – “Management at a Glance”
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Micro‐Pulse Irrigation Scheduling Capability
Sensor‐based irrigation control scheduled for 15 minutes every hour Within each 15 minute period, able to irrigate up to three, 4‐minute pulses (i.e. 240s on, 60s off) But it ONLY irrigates if the minimum substrate soil moisture set‐point is reached
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Micro‐Pulse Irrigation Capability
• Measures Variability– Averaging Capability (any sensor, any node)
• Acts on that Information – Automated Micro‐pulse Irrigation Only irrigates when average soil
moisture is below a set value If average is not increased after
and irrigation, the system will pulse again after set time (0 ‐ 300 seconds)
• Minimizes Irrigation and Leaching micropulse control shown to
reduce water applications by 40‐70% and nutrient leaching typically by at least 50%
Kohanbash, Kantor, Martin and Crawford, 2013 HortTechnology 23: 725‐734
Enables remote and/or automatic control of irrigation schedules, via a customizable web‐based interface
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Grower‐Controlled Drip Irrigation (Strawberry Production)
Grower water applications from March – May were typically 4 or 5 (15 min pulses) per day. Last pulse event (typically 6 – 8pm) often resulted in over‐saturation and nutrient leaching
below 35cm (green line)
Guéry, S., J. Lea‐Cox, M. Martinez Bastida, B. Belayneh and F. Ferrer‐Alegre, Acta Hort 2016
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Sensor‐Controlled Drip Irrigation (Strawberry Production)
Sensor‐controlled irrigations were typically 2‐5 (four minute pulses) per day. Total water use (Jan through May) = 58.8% less than grower‐applied
(328,185 Gals / acre vs. 795,587 Gals / acre)
Guéry, S., J. Lea‐Cox, M. Martinez Bastida, B. Belayneh and F. Ferrer‐Alegre, Acta Hort 2016
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Pore Water EC
15cm VWC
35cm VWC
Distributed Sensing: Measuring Nutrient Leachate
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Sensor‐Controlled Dynamics Over Time
Belayneh, Lea‐Cox and Lichtenberg, 2013. HortTechnology 23:760‐769
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Irrigation Water Applied: March – Nov, 2012 (Cornus florida)
Irrigation Method
Total WaterApplied
(Liters / Row)
Average Water Application
(L / Tree /Day)
Av. Efficiency (Timed vs. Control)
Water Savings (Control vs. Timed)
Grower: Timed, Cyclic 109,794 3.49
0.371 2.69 Sensor: Set‐point
Control 40,769 1.30
Belayneh, Lea‐Cox and Lichtenberg, 2013. HortTechnology 23:760‐769
Irrigation Efficiency – Water Savings
Irrigation water applications reduced between 40 and 70% depending upon species, site‐specific variables and time of year
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Belayneh, Lea‐Cox and Lichtenberg, 2013. HortTechnology 23:760‐769
Irrigation Efficiency – Return on Investment
2.7 year ROI 4‐month ROI
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Reduction in Production Times, Net Benefits
14‐month production cyclecollapsed to 8‐month
30% loss to Disease reducedto virtually zero
Economic Gain = $1.06 / ft2(total net revenue = $20,700 for crop)
ROI < 3 months for $6,000network
Lichtenberg, Chappell et al., 2013 HortTechnology 23:770‐776
Gardenia ‘radicans’ ‐ high shrinkage due to disease /unmarketable final product.
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35,000 square foot greenhouse production • Produces 475,000 stems of Cut Snapdragons, Sunflowers per annum• Hydroponic culture using recirculating water and nutrients• Perlite substrate in bags, monitored with EC‐5, GS3‐EC sensors; Tank with EC, pH • Canopy environment monitored with air Temp / RH (VPD) and light (PAR) sensors
Increase in Crop Quantity and Quality
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2007 ‐2009 2010‐ 2012 Difference Change
Crops/ year 37 38 1 1 %
Stems/ year 106,173 156,320 50,147 47.2 %
Price/ stem $ 0.565 $ 0.596 $ 0.031 + 5.5 %
Labor costs $ 14,976 $ 18,961 $ 3,985 + 26.6 %
Electricity $ 3,838 $3,538 $ 300 ‐7.8 %
Sensor system(Annualized over 3 years)
$ 0 $7,147 $7,147 ‐‐‐
Net Revenue $40,317 $ 65,173 $24,856 + 61.7 %
Profit $21,503 $35,526 $14,636 + 65.2 %
Payback period on upfront costs: <8 months
Cut‐flower Snapdragon: Annual Profitability
Pre‐Sensor: (2007 – 2009) Post‐Sensor: (2010 – 2012)
Lichtenberg et al., 2016 (Irrigation Sci. 34: 409‐420)
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Alert Capability and Fault Detection
Alerts can be set for any node or sensor, at a specified time or set‐point threshold value
Alerts can be texted or emailed to any user
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Photosynthetic and Total radiation
Leaf wetness, Dew and Ice
Precipitation
temperature, RHand VPD
Wind speed and direction
Sonic anemometer
Environmental Sensors
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Environmental Data Applications
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Predictive Tools for IPM, Disease Management
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Smart-Farms.net ― Managing Irrigation and Nutrition via Distributed Sensing
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Summary
New sensor tools and technologies allow for precision control of water and nutrient applications
Sensor networks can provide significant water savings for growers using controlled environments
Benefits of better water management are multiple and synergistic
Returns on investment are typically < 1 year, especially when cost of water / remediation is high