Using Sensor Networks for Precision Water and Nutrient Management in Controlled Environments

Transcription

1 Using Sensor Networks for Precision Water and Nutrient Management in Controlled Environments John D. Lea Cox, PhD Systematic Sensing, LLC

2 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

3 Sensor SCRI MINDS: NetworksTeams 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) Smart-Farms.net Managing Irrigation and Nutrition via Distributed Sensing 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 )

4 Smart Toolbox

5 PlantPoint Irrigation Control Hardware

6 Soil Moisture, EC and Irrigation Sensors Various soil moisture sensors Soil EC, moisture and temperature In line Water Pressure In line, Tank EC Flow Meters

7 Environmental Sensors Photosynthetic and Total radiation Wind speed and direction Precipitation temperature, RH and VPD Sonic anemometer Leaf wetness, Dew and Ice

8 SensorWeb Software Interface Operation s Homepage Management at a Glance

9 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

10 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: Enables remote and/or automatic control of irrigation schedules, via a customizable web based interface

11 Grower Controlled Drip Irrigation (Strawberry Production) Guéry, S., J. Lea Cox, M. Martinez Bastida, B. Belayneh and F. Ferrer Alegre, Acta Hort 2016 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)

12 Sensor Controlled Drip Irrigation (Strawberry Production) Guéry, S., J. Lea Cox, M. Martinez Bastida, B. Belayneh and F. Ferrer Alegre, Acta Hort 2016 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)

13 Distributed Sensing: Measuring Nutrient Leachate 15cm VWC 35cm VWC Pore Water EC

14 Sensor Controlled Dynamics Over Time Belayneh, Lea Cox and Lichtenberg, HortTechnology 23:

15 Irrigation Efficiency Water Savings Irrigation Water Applied: March Nov, 2012 (Cornus florida) Irrigation Method Total Water Applied (Liters / Row) Average Water Application (L / Tree /Day) Av. Efficiency (Timed vs. Control) Water Savings (Control vs. Timed) Grower: Timed, Cyclic Sensor: Set point Control 109, , Irrigation water applications reduced between 40 and 70% depending upon species, site specific variables and time of year Belayneh, Lea Cox and Lichtenberg, HortTechnology 23:

16 Irrigation Efficiency Return on Investment 2.7 year ROI 4 month ROI Belayneh, Lea Cox and Lichtenberg, HortTechnology 23:

17 Reduction in Production Times, Net Benefits Gardenia radicans high shrinkage due to disease /unmarketable final product. 14 month production cycle collapsed to 8 month 30% loss to Disease reduced to virtually zero Economic Gain = $1.06 / ft 2 (total net revenue = $20,700 for crop) ROI < 3 months for $6,000 network Lichtenberg, Chappell et al., 2013 HortTechnology 23:

18 Increase in Crop Quantity and Quality 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

19 Cut flower Snapdragon: Annual Profitability Pre Sensor: ( ) Post Sensor: ( ) Difference Change Crops/ year % Stems/ year 106, ,320 50, % Price/ stem $ $ $ % Labor costs $ 14,976 $ 18,961 $ 3, % Electricity $ 3,838 $3,538 $ % Sensor system (Annualized over 3 years) $ 0 $7,147 $7,147 Net Revenue $40,317 $ 65,173 $24, % Profit $21,503 $35,526 $14, % Payback period on upfront costs: <8 months Lichtenberg et al., 2016 (Irrigation Sci. 34: )

20 Alert Capability and Fault Detection Alerts can be set for any node or sensor, at a specified time or setpoint threshold value Alerts can be texted or ed to any user

21 Environmental Sensors Photosynthetic and Total radiation Wind speed and direction Precipitation temperature, RH and VPD Sonic anemometer Leaf wetness, Dew and Ice

22 Environmental Data Applications

23 Predictive Tools for IPM, Disease Management

24 Smart-Farms.net Managing Irrigation and Nutrition via Distributed Sensing

25 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