SENSOR-BASED AUTOMATION OF IRRIGATION OF
SENSOR-BASED AUTOMATION OF IRRIGATION OF BERMUDAGRASSByBERNARD CARDENAS-LAILHACARA THESIS PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEUNIVERSITY OF FLORIDA2006
Copyright 2006byBernard Cardenas-Lailhacar
To my parents and sons
ACKNOWLEDGMENTSIn the first place, I wish to thank my parents for their always enormous andunconditional love, support, and guidance; my sons for being the most adorable humanbeings I have ever met; my ex-wife for taking care of them while I was completing mystudies, her understanding, patience, and sacrifice; and my amorcita for her enormoussupport, understanding, patience and, most of all, her immense love. Next, I would like tothank all my thesis committee members for being not just professors, but great humanbeings: Dr. Dorota Z. Haman and Dr. Grady L. Miller, also for their guidance andpatience, and a huge thank you goes to Dr. Michael D. Dukes, for giving me theopportunity to work with him, which was always a lot of work, but also a pleasure. Also,I wish to give a special thank you to Melissa B. Haley for being always ready to help me.Lastly, I would also like to thank Engineer Larry Miller, Senior Engineering TechnicianDanny Burch, and students Mary Shedd, Stephen Hanks, Clay Coarsey, Brent Addison,Jason Frank, and Clay Breazeale for their assistance on this research. This research wassupported by the Pinellas-Anclotte Basin Board of the Southwest Water ManagementDistrict, the Florida Nursery and Landscape Growers Association, and the FloridaAgricultural Experiment Station.iv
TABLE OF CONTENTSpageACKNOWLEDGMENTS . ivLIST OF TABLES. viiiLIST OF FIGURES .xABSTRACT.xvCHAPTER1INTRODUCTION .1Water.1Water Demand .2Water Use .2Water Use Restrictions .3Landscapes in Florida .5Irrigation .6Irrigation Timers.6Soil Moisture Content Measurement.8Granular matrix sensor .8Modern soil moisture sensors.9Controllers .11Automatic Control of Irrigation.12Rain Sensors .13Irrigation and Turfgrass Quality .152SENSOR-BASED AUTOMATION OF IRRIGATION OF BERMUDAGRASS .20Introduction.20Materials and Methods .25Treatments .27Uniformity Test .28Dry-Wet Analysis.29Plot Irrigation Management and Data Collection.30Data Analysis.33Results and Discussion .34Uniformity Tests.34v
Dry-Wet Analysis.34Rainfall .35Irrigation Events .36Irrigation Application Comparisons .42Time-based treatments vs. SMS-based treatments.42Time-based treatments .42Comparisons between SMS-irrigation frequencies.44Soil moisture sensor-brands comparison.45Brand comparisons within irrigation frequencies .46Overall comparison .47Automation of Irrigation Systems .49Turfgrass Quality.50Summary and Conclusions .513EXPANDING DISK RAIN SENSOR PERFORMANCE AND POTENTIALIRRIGATION WATER SAVINGS .93Rain Sensors .93Advantages .94Types and Methods.94Installation .96Objectives .96Materials and Methods .97Data.97Treatments .98Statistical Analysis .99Results and Discussion .99Climatic Conditions.99Number of Times in Bypass Mode.99Depth of Rainfall Before Shut Off .100Duration in Irrigation Bypass Mode (Dry-Out Period) .102Potential Water Savings .103Payback Period .104Summary and Conclusions .1054GRANULAR MATRIX SENSOR PERFORMANCE COMPARED TOTENSIOMETER IN A SANDY SOIL.119Tensiometers.119Granular Matrix Sensors.121GMS – Tensiometer Comparison .122Objectives .122Materials and Methods .122Experimental Set-Up .123ECH2O Probes Calibration .123Treatments .124Data.124vi
Results and Discussion .125Calibration of the ECH2O Probe .125GMSs versus Tensiometers. .125Conclusions.1265CONCLUSIONS AND FUTURE WORK.140Conclusions.140Future Work.142APPENDIXALIST OF ABBREVIATIONS.144BSTATISTICAL ANALYSES .145LIST OF REFERENCES.199BIOGRAPHICAL SKETCH .208vii
LIST OF TABLESpageTable2-1. Irrigation treatment codes and descriptions.542-2. Monthly irrigation depth to replace historical evapotranspiration, assumingsystem efficiency of 60%, and considering effective rainfall. .542-3. Total number and percent of overridden scheduled irrigation cycles; 2004 and2005.552-4. Percent of irrigation cycles allowed by the SMS-based treatments through theexperimental months of 2004 and 2005. .562-5. Cumulative irrigation depth applied to treatments, statistical comparisonsbetween them, and percent of water savings compared to 2-WRS, 2-DWRS, and2-WORS; year 2004. .572-6. Cumulative irrigation depth applied to treatments, statistical comparisonsbetween them, and percent of water savings compared to 2-WRS, 2-DWRS, and2-WORS; year 2005. .582-7. Total cumulative irrigation depth applied to treatments, statistical comparisonsbetween them, and percent of water savings compared to 2-WRS, 2-DWRS, and2-WORS; years 2004 2005. .593-1. Treatments description. .1073-2. Average depth of rainfall before rain sensors switched to bypass mode.1073-3. Large rainfall events not bypassed by treatment 3-MC.1073-4. Large rainfall events not bypassed by treatment 13-MC.1083-5. Large rainfall events not bypassed by treatment 25-MC.1083-6. Hours after rain stopped and sensors switched to bypass mode; treatment 3-MC. .1083-7. Hours after rain stopped and sensors switched to bypass mode; treatment 13-MC.109viii
3-8. WL replications that switched to bypass mode in absence of rainfall, elapsed timethat they remained in bypass mode, and relative humidity at the time when thisoccurred.1093-9. Total potential water savings per treatment.1093-10. Potential payback period per treatment. .1104-1. Treatments. .1284-2. GMS-Tensiometer crossing points. .128ix
LIST OF FIGURESpageFigure1-1. Components of an automated irrigation system: A) timer, B) power supply, C)soil moisture sensor-controller circuitry, D) soil moisture sensor, and E)solenoid valve.161-2. Granular matrix sensors (GMS) .171-3. Components of an automated irrigation system. 1) Timer, and 2) soil moisturesensor-controllers from different brands. .181-4. Rain shut-off switch.191-5. The expanding material of a rain shut-off switch.192-1. Soil water retention curve from tensiometers and calibrated ECH2O probereadings. .602-2. Soil moisture sensor brands tested in this study. .612-3. Irrigation controls as installed for this study: soil moisture sensors-controllersbrands: A) Rain Bird, B) Water Watcher, C) Acclima, and D) Irrometer, andirrigation timer E) Rain Bird. .622-4. Rain sensor installed for this study.632-5. Catch-can display for uniformity tests on turfgrass plots.642-6. General view of the irrigation controls used in this study.652-7. Pipes, flowmeters, valves, and wirings for this study. .652-8. Control board showing timers, soil moisture sensor-controllers, solenoid valveswiring, and flowmeters-datalogger (details are shown in the next s).662-9. Control board detail showing the solenoid valves control box. .672-10. Control board detail, flowmeter-datalogger boxes showing A) multiplexers, B)CR 10X datalogger used for this study. .672-11. Automated weather station near turf plots for this study.68x
2-12. ECH2O probe, capacitance soil moisture probe shown with a HOBO data loggeras installed for this study.692-13. Plot plan showing the low-quarter distribution uniformity testing results on eachplot.702-14. Plot plan showing average volumetric water content (%) on each plot during arelatively “dry” period. Plots in red were discarded, and plots in green were usedfor placement of SMSs. .712-15. Plot plan showing average volumetric water content (%) on each plot during arelatively “wet” condition. Plots in red were discarded, and plots in green wereused for placement of SMSs.722-16. Plot plan with the modified completely randomized design (same color depictstreatment repetitions).732-17. Daily and cumulative rainfall in 2004. Note: rainfall for 5 Sep. (188 mm) and 6Sep. (81 mm) is shown as a cumulative total (269 mm). .742-18. Daily and cumulative rainfall in 2005. .742-19. Cumulative number of irrigation events per treatment in 2004; A) time-basedtreatments, and soil moisture sensor-based treatments at irrigation frequencies ofB) 1 d/w, C) 2 d/w, and D) 7 d/w.752-20 Cumulative number of irrigation events per treatment in 2005; A) time-basedtreatments, and soil moisture sensor-based treatments at irrigation frequencies ofB) 1 d/w, C) 2 d/w, and D) 7 d/w.762-21. Maximum weekly irrigation water requirement (rainfall - ETo difference); year2004.772-22. Maximum weekly irrigation water requirement (rainfall - ETo difference); year2005.772-23. Volumetric moisture content through time, on treatment 0-NI, year 2004. .782-24. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 1-AC, year 2004. .792-25. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 1-IM, year 2004.802-26. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 1-RB, year 2004. .81xi
2-27. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 1-WW, year 2004. .822-28. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 2-AC, year 2004. .832-29. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 2-IM, year 2004.842-30. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 2-RB, year 2004. .852-31. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 2-WW, year 2004. .862-32. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 7-AC, year 2004. .872-33. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 7-IM, year 2004.882-34. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 7-RB, year 2004. .892-35. Volumetric moisture content (VMC) through time, showing results of thescheduled irrigation cycles (SIC); treatment 7-WW, year 2004. .902-36. Average irrigation depth applied by brand; years 2004 and 2005 (P 0.0001).912-37. View of different plots where no evident turfgrass quality differences could bedetected; A) good quality, B) dormant.923-1. Mini-Click (Hunter Industries, Inc.) rain sensor. A) Rain threshold set slots, B)vent ring.1113-2. Wireless Rain-Click (Hunter Industries, Inc.) rain sensor. A) Ventilation windowadjustment knob, B) ventilation windows, C) antenna. .1113-3. The expanding material of a rain shut-off switch.1123-4. Rain sensor experiment layout: A) Wireless Rain-Click rain sensors, B) MiniClick rain sensors, C) Wireless Rain-Click receivers, D) multiplexers, E) CR10X datalogger. .1123-5. Manual rain gauge measurements compared to tipping bucket rain gaugemeasurements. .1133-6. Daily and cumulative rainfall. .113xii
3-7. Cumulative number of times rain sensors switched to bypass mode; average pertreatment. Different letters indicate a significant difference by Duncan’sMultiple Range Test (P 0.05).1143-8. Cumulative number of times rain sensors switched to bypass mode; WLtreatment, with replicates indicated by A-D.1153-9. Cumulative number of times rain sensors switched to bypass mode; 3-MCtreatment, with replicates indicated by A-D.1153-10. Cumulative number of times rain sensors switched to bypass mode; 13-MCtreatment, with replicates indicated by A-D.1163-11. Cumulative number of times rain sensors switched to bypass mode; 25-MCtreatment, with replicates indicated by A-D.1163-12. Histogram and frequency distribution for 6-hour intervals in bypass mode; WL.1173-13. Histogram and frequency distribution for 6-hour intervals in bypass mode; 3MC.1173-14. Histogram and frequency distribution for 6-hour intervals in bypass mode; 13MC.1184-1. MLT-RSU Tensiometer.1294-2. Watermark GMS.1294-3. Temperature sensor.1304-4. ECH2O probe.1304-5. Experimental layout (top view). A) Tensiometers, B) Granular matrix sensors, C)ECH2O probe, and D) Thermometer.1314-6. Watermark monitor.1324-7. ECH2O probe hooked up to a HOBO Micro Station datalogger.1324-8. Volumetric moisture content (VMC) from all three ECH2O probes compared togravimetric measurements.1334-9. Soil water tension through time; treatment T0. .1344-10. Soil water tension through time; treatment T5. .1344-11. Soil water tension through time; treatment T15. .1354-12. Soil water tension through time; treatment T50. .135xiii
4-13. Soil water tension through time; detail showing when curves from GMS andtensiometers cross; treatment T0.1364-14. Soil water tension through time; detail showing when curves from GMS andtensiometers cross; treatment T5.1364-15. Soil water tension through time; detail showing when curves from GMS andtensiometers cross; treatment T15.1374-16. Soil water tension through time; detail showing when curves from GMS andtensiometers cross; treatment T50.1374-17. Relation between the average soil matric potential (SMP) from tensiometers andGMS. .1384-18. Relation between the average soil matric potential (SMP) from tensiometers andGMS; excluding GMS data 10 kPa.139xiv
Abstract of Thesis Presented to the Graduate Schoolof the University of Florida in Partial Fulfillment of theRequirements for the Degree of Master of ScienceSENSOR-BASED AUTOMATION OF IRRIGATION OF BERMUDAGRASSByBernard Cardenas-LailhacarAugust 2006Chair: Michael D. DukesMajor Department: Agricultural and Biological EngineeringTurfgrass in landscapes contributes to substantial cropped area in Florida. Newirrigation technologies could improve irrigation efficiency, promoting water conservationand reducing the environmental impacts. The objectives of this research were to quantifydifferences in irrigation water use and turf quality among 1) a soil moisture sensor-basedirrigation system compared to a time-based scheduling, 2) different commercial irrigationsoil moisture sensor (SMSs), 3) a time-based scheduling system with or without a rainsensor (RS), and 4) the reliability of two commercially available expanding disk RStypes. The experimental area consisted of common bermudagrass (Cynodon dactylon L.)plots (3.66 x 3.66 m) in a completely randomized design, located in Gainesville, Florida.The monitoring period for the irrigation treatments took place from 20 July through 14December of 2004 and from 25 March through 31 August of 2005. Treatments consistedof irrigating one, two, or seven days a week, each with four different commercial SMSsbrands. A non-irrigated control and time-based treatments were also implemented. Inxv
addition, twelve Mini-Click (MC) and four Wireless Rain-Click (WL) rain sensor modelsnot connected to irrigation were monitored from 25 March through 31 December 2005.For the MCs, three different thresholds were established: 3, 13, and 25 mm (codes 3-MC,13-MC, and 25-MC, respectively). No significant differences in turfgrass quality amongirrigation treatments were detected. On average, SMS-based treatments reduced irrigationwater application compared to time-based treatments. The treatment without-rain-sensor(2-WORS) used significantly (52%) more water than the with-rain-sensor treatment (2WRS). Most brands recorded significant irrigation water savings compared to 2-WRS,which ranged from 54% to 88%, for the best performing sensors, and depending on theirrigation frequency. Therefore SMS-systems represent a promising technology, becauseof the water savings that they can accomplish, while maintaining an acceptable turfgrassquality during rainy periods (944 and 732 mm of rainfall, for seasons 2004 and 2005,respectively). On average, RS treatments WL, 3-MC, 13-MC, and 25-MC respondedclose to their rainfall set points (1.4, 3.4, 10.0, and 24.5 mm, respectively). However,some replications showed erratic behavior through time. The number of times that thesesensors shut off irrigation was inversely proportional to the magnitude of their set point(81, 43, 30, and 8 times, respectively) with potential water savings following a similartrend (363, 245, 142, and 25 mm, respectively). Under the relatively wet testingconditions typical to Florida, the payback period could be less than a year, except for 25MC (around 7 years). Consequently, RSs are strongly recommended for use byhomeowners as a means to save water, but not when accuracy is required.xvi
CHAPTER 1INTRODUCTIONTurfgrass is the main cultivated crop in Florida with nearly four times the acreageas the next largest crop, citrus (Hodges et al., 1994; United States Department
2-2. Soil moisture sensor brands tested in this study.61 2-3. Irrigation controls as installed for this study: soil moistur
ZigBee, Z-Wave, Wi -SUN : . temperature sensor, humidity sensor, rain sensor, water level sensor, bath water level sensor, bath heating status sensor, water leak sensor, water overflow sensor, fire sensor, cigarette smoke sensor, CO2 sensor, gas s
WM132382 D 93 SENSOR & 2 SCREWS KIT, contains SENSOR 131856 WM132484 B 100 SENSOR & 2 SCREWS KIT, contains SENSOR 131272 WM132736 D 88 SENSOR & HARNESS, contains SENSOR 131779 WM132737 D 100 SENSOR & 2 SCREWS KIT, contains SENSOR 131779 WM132739 D 100 SENSOR & 2 SCREWS KIT, contains SENSOR 132445 WM147BC D 18 RELAY VLV-H.P.-N.C., #WM111526
Chapter 9 Irrigation Water Management Part 652 Irrigation Guide (210-vi-NEH 652, IG Amend. NJ1, 06/2005) NJ9-1 NJ652.09 Irrigation Water Management (a) General Irrigation water management is the act of timing and regulating irrigation water applications in a way that will satisfy the
The Irrigation Requirement is the Crop Water Requirement after taking into account the irrigation efficiency and, for drip systems, the drip factor: IR CWR * Df / Ie For irrigation systems other than drip, the drip factor 1. 6. Irrigation Water Demand (IWDperc and IWD) The portion of the Irrigation Water Demand lost to deep percolation is .
SMART IRRIGATION: THE CONCEPT Automatically adjust irrigation run times in response to environmental changes A well-designed 'smart irrigation'system can be 95% efficient matching actual daily plant water requirements to realise water use savings of 40%. Household water use for garden irrigation reduced Water used for POS and verge irrigation
Trickle/Drip Irrigation Worksheet Drip irrigation zones may cover a larger area than sprinkler irrigation zones. The calculations for drip systems are based on each zone. Zones should be designed so the crop type, crop maturity and soil type are the same through the entire zone. Considerations for drip irrigation system chemigation are:
sensor terminals. 4. Connect the white and green wires to the sensor inputs. 5. Reconnect power to the irrigation controller.! Ensure the sensor switch on the irrigation controller panel is in the active position. Controllers with no sensor inputs (with or without pump start / master va
programmable logic controller, is important for industrial engineer. Factory automation mainly covers; Machine level automation, Production line or work cell automation, Shop floor automation, and Plant level automation. The present manual focus on the 1st level of factory automation e.g. machine automation level. It provides an introduction .
