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Computers and Electronics in Agriculture 48 (2005) 183–197

An autonomous controller for site-specificmanagement of fixed irrigation systems

F.R. Mirandaa, R.E. Yoderb,∗, J.B. Wilkersonc, L.O. Odhiamboc

a Embrapa Tropical Agro-Industry, C.P. 3761, Fortaleza, CE 60511-110, Brazilb Biological Systems Engineering Department, University of Nebraska, P.O. Box 830726,

Lincoln, NE 68583-0726, USAc Department of Biosystems Engineering and Environmental Science, The University of Tennessee,

2506 E. J. Chapman Drive, Knoxville, TN 37996-4531, USA

Received 6 October 2004; received in revised form 16 March 2005; accepted 1 April 2005

Abstract

Distributed irrigation control (DIC) for site-specific management and/or operation of fixed irrigationsystems is easier to install and maintain as compared to centralized irrigation control (CIC), but requiresmultiple controllers in the field. The advantages of DIC over CIC systems include: (1) reduced wiringand piping requirements, (2) a lower risk of complete system failure due to mechanical damage orlightening strikes, and (3) more flexibility when modifying or extending the system. In this study,a low cost solar-powered feedback controller for DIC of fixed irrigation systems was developedand tested. The specific tasks included the controller design (hardware and software), performanceevaluation, and power optimization. The controller uses soil water potential (SWP) measurementsto control the amount of water applied to each specific management area of a field, and measuredsystem hydraulic pressure to communicate with other controllers. Each controller is autonomouslypowered by a solar panel and battery, eliminating hard-wire connections among control units. Theresults indicate that the controller was effective in maintaining the SWP in the root zone close to apredetermined management allowed deficit (MAD). The power supply was optimized using simulatedand measured solar radiation data from two locations.© 2005 Elsevier B.V. All rights reserved.

Keywords: Irrigation; Site-specific; Spatially variable; Distributed control; Moisture sensor

∗ Corresponding author. Tel.: +1 402 472 1413; fax: +1 402 472 6338.E-mail address:[email protected] (R.E. Yoder).

0168-1699/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.compag.2005.04.003

184 F.R. Miranda et al. / Computers and Electronics in Agriculture 48 (2005) 183–197

1. Introduction

Many irrigation scheduling methods have been developed over the years, but adoptionby producers has been limited by cost, installation time, maintenance, and complexityof the decisions involved. A potential solution to these problems is total automation ofirrigation using feedback control systems. For example, electronic feedback soil moisturesensors installed in the crop root zone have been used to accurately control high-frequencyirrigation (Phene and Howell, 1984).

Site-specific irrigation management can be implemented using spatially variable irri-gation systems to optimize yields and maximize water use efficiency for fields withvariation in water availability due to different soil characteristics or crop water needs.Site-specific irrigation management is more likely to be economically viable for high-value crops. A field study carried out byKing et al. (2002)demonstrated the eco-nomic benefits of site-specific irrigation management on potatoes, where it increasedtotal yield, marketable yield, and gross income relative to conventional uniform irriga-tion management. Spatially variable irrigation systems previously developed have typi-cally used self-propelled irrigation systems, such as center-pivots and linear moves, asthe platform for sensing and control (Buchleiter et al., 1995; Fraisse et al., 1995a,b;Sadler et al., 1996, 2000; Wall et al., 1996; McCann et al., 1997; King et al.,1999).

To control irrigation of small areas in a field, spatially variable control of fixed irriga-tion systems such as solid-set sprinkler and microirrigation requires a network capable ofcontrolling a large number of sensors and valves. This can be achieved by using cen-tralized or distributed irrigation control. A centralized irrigation control system (CIC)connects individual sensors and actuators to a centrally located controller by point-to-point communication using either direct wiring or radio frequency (RF) or infrared (IR)links. In the case of direct wiring links, CIC becomes expensive, difficult to main-tain, and lacks flexibility, especially for site-specific irrigation control in large irrigatedfields. Depending upon the distance between individual sensors and actuators to a cen-trally located controller, RF or IR links could be cheaper than point-to-point wiring.Distributed irrigation control (DIC) systems, on the other hand, have autonomous con-trollers in discrete locations close to sensors covering relatively homogeneous areas inthe field. These autonomous controllers have some intercommunication, which allowthe system to prioritize irrigation decisions between site-specific irrigation managementunits. The advantages of DIC are reduced wiring and piping costs, and easier instal-lation and maintenance (Torre-Neto et al., 2000). However, since additional controllerunits are required for DIC, this type of system is viable for site-specific irrigation onlyif low-cost controllers with low-power components (sensors, actuators, etc.) are avail-able.

Most commercially available sensors and actuators assembled for irrigation system net-works are too complex and/or costly to be feasible for site-specific management of fixedirrigation systems. The objectives of this research were to develop and test an autonomous,low cost, feedback irrigation controller for site-specific management of fixed irrigationsystems.

F.R. Miranda et al. / Computers and Electronics in Agriculture 48 (2005) 183–197 185

2. Irrigation controller description

The irrigation controller developed in this study is designed to work autonomously with-out hard-wire connections between individual control units. Each controller is designedto operate on solar power. A battery is used to balance the differences between avail-able solar power and power demand on an hour-to-hour basis. For site-specific irriga-tion implementation, a field is typically divided into irrigation management units basedon soil characteristics, crop water requirements, and/or economic factors prior to theinstallation of the control system. An irrigation controller is installed in each irrigationmanagement unit to autonomously control the soil water potential (SWP) in the croproot zone between field capacity (FC) and management allowed deficit (MAD) set bythe user. Each controller is programmed to process the feedback information receivedfrom three SWP sensors that are installed in the root zone within the irrigation man-agement unit. When two of the sensors indicate that the SWP is more negative than theMAD, the irrigation controller opens a solenoid valve, triggering irrigation of the manage-ment unit. Irrigation continues until two of the three soil sensors indicate that the SWPexceeds the MAD. Soil moisture determinations and irrigation decisions occur at fixedregular intervals set by the user. Real-time clocks for all irrigation management units’ con-trollers are synchronized for measurement of the SWP, and irrigation decisions at 15-minintervals.

Priority irrigation scheduling is used to optimize water allocation among the site-specificirrigation management units. This consists of the irrigator setting the irrigation priority ofeach irrigation management unit based on the cost-to-benefit ratio of irrigating the unitsand/or crop sensitivity to water stress in case of multiple crop fields. When the waterdemand is greater than the maximum flow capacity of the irrigation system, the irrigationmanagement units with the highest priority are irrigated first. The irrigation managementunits with lower priorities are irrigated after irrigation requirements are met for the unitswith higher priorities.

The irrigation controllers perform priority irrigation scheduling among irrigation man-agement units by using the hydraulic pressure measured in the main system pipeline as acommunication bus. The effects of pipeline length, pipe material, and elevation are takeninto account so that the measured hydraulic pressure reflects only the changes due to waterflow in the pipeline. Each controller monitors the pressure in the main pipeline after startof irrigation events and if the pressure is below a preset threshold value, the controllers areprogrammed to sequentially close the irrigation valves, beginning with the lowest priorityirrigation management unit until the pressure exceeds the threshold value (Miranda, 2003).This sequential closing of irrigation valves allows irrigation requirements of the manage-ment units with the higher priorities to be met before irrigating the management units withlower priorities

After sensor measurements and irrigation decisions are made, the controller storesthe corresponding data that include date, time, soil temperature, SWP, hydraulic pres-sure, and valve status data. Data are stored in non-volatile memory to prevent lossif a power failure occurs. The user can download recorded data to analyze systemperformance.


Fig. 1. Block diagram of irrigation control system developed in this study.

2.1. Controller hardware

Electronic devices, sensors, and actuators were selected to meet the low power andlow cost required for the DIC system. A block diagram of the DIC hardware is shownin Fig. 1. The sub-components of the controller unit are a microcontroller, real-timeclock, non-volatile data storage, an analog-to-digital (A/D) converter, and a battery charger.Each irrigation controller monitors SWP, soil temperature, system pressure, and controls asolenoid valve for irrigation. A photograph of the complete irrigation controller is shown inFig. 2, and a photograph of the controller interfaced with sensors and the actuator is shownin Fig. 3.

The microcontroller unit (MCU) consists of a BASIC Stamp II unit (Parallax, Inc.,Rocklin, CA) and is the master device that is programmed to keep time, communicatewith data storage device, read sensors, and control the actuators. The MCU has a 20-MHzPIC processor (PIC16C57, Microchip Tech., Inc. Chandler, AZ) with 32 bytes of internalRAM, and is a 24-pin DIP package with 16 programmable I/O pins (TTL-level), and twoadditional pins dedicated to asynchronous communications. This MCU was selected basedon low-cost, processor speed, low power requirements, rapid software development, andease of system integration with custom circuits.

The sensor used to monitor SWP in the root zone was chosen for accuracy, reliabil-ity, durability, low maintenance, ease of interfacing with data acquisition systems, andlow cost. Although no existing soil sensor scores high in all these areas, the Watermark®

sensor (Model 200SS, Irrometer Co., Riverside, CA) was selected as the best option. TheWatermark® sensor is relatively maintenance free, and exhibits a good combination of price,accuracy, and reliability (Yoder et al., 1998). It is a resistance type sensor, which consistsof two concentric electrodes embedded in a porous matrix. Once in hydraulic contact withthe soil, the porous matrix absorbs or releases water in response to soil matric potentialgradients until equilibrium is reached. The electrical resistance of the material betweenthe electrodes is a function of the SWP with which the porous matrix is at equilibrium,increasing as the SWP becomes more negative.


Fig. 2. Controller developed in this study for distributed irrigation control.

To measure the Watermark® sensor resistance, a resistor/capacitor (RC) timing circuitis used in the MCU (Fig. 4). Initially the MCU discharges the capacitor until both sidesmeasure 5 V with respect to the ground, after which the I/O pin is set as an input and theMCU measures the time it takes for the voltage seen by the I/O pin to drop from 5 to 1.5 V(initial voltage to threshold voltage). The electrical potential across the sensor is driven toground between measurements preventing polarization of the sensor. The time measurement(clock cycles) is detected using the “RCTime” function. Sensor resistance can be calculatedusing the equation:

R = RCTime× k

ln(Vi /Vt)C(1)

whereRis the sensor resistance,k the timing constant for the controller,Vi the initial voltage,Vt the threshold voltage, andC is the capacitance. The sensor resistance (R) was convertedto SWP using the following calibration equation presented byShock et al. (1996):

SWP= −(2.678+ 0.003892× R)

1 − 0.01201× T(2)


Fig. 3. View of the irrigation controller showing: (1) irrigation controller; (2) solar panel; (3) soil water potentialsensor; (4) pressure sensor; (5) solenoid valve.

Fig. 4. Resistor/capacitor circuit used by the irrigation controller to read resistance type sensors.


where SWP is the soil water potential in kPa,R the resistance of the Watermark® in �, andT is the soil temperature in◦C. The soil temperature was measured using a thermistor. Athermistor model C100F103G (Thermometrics, Inc., Plainville, CT) was chosen becauseit is a low-cost solid-state temperature sensor, with high sensitivity in the typical rangeof soil temperatures (−10 to 50◦C). Temperature measurements using the thermistor areperformed by the MCU through the same RC circuit that is used for reading the SWPsensors.

The location of SWP sensors in the field is critical for successful implementation ofsite-specific irrigation control. Enough sensor stations are installed in the field to properlyrepresent varying conditions and they are placed where most of the roots are located. Asensor station is placed in each representative location of the field that has differing terrain,soil type, or crop. For large, fairly uniform field units, at least two sensor stations should beinstalled. The depths to place the sensors will depend on the rooting characteristics of thecrop being irrigated. For deeper rooting crops, each sensor station should contain at leasttwo sensor units placed at different depths in the root zone. The upper sensor should beplaced at the center of the effective root zones and the lower sensor should be placed near thebottom of that root zone (Thomson and Ross, 1996). Early in the season, the shallow sensoris used to trigger irrigation. As the plant grows and roots migrate downward, readings fromboth the shallow and deeper sensors are used to trigger irrigation. The deeper sensor canalso be used to estimate the amount of deep percolation losses after irrigation. For shallowrooting crops, however, only one sensor placed at the center of the effective root zone isneeded to indicate when to irrigate.

The irrigation controller uses a piezoresistive pressure transducer (MPX5700GP,Motorola, Inc., Denver, CO) to monitor the hydraulic pressure in the irrigation pipeline.The pressure transducer signal output is measured by the MCU using a serial A/D con-verter. The A/D conversion is a 12-bit, two-channel analog-to-digital converter (LTC1298,Linear Technology, Inc., Milpitas, CA), with a 1.22 mV resolution over a full-scale voltageinput of 0–5 V DC.

To minimize system power requirements, sensors are activated only when measurementsare being made. Furthermore, latching valves that have low power requirements were usedin the system. The latching solenoid valve model TBOSPSOL DV-100-SS (Rain Bird Corp.,Azusa, CA) that was selected to control the water flow, is well suited for low-power, battery-operated irrigation controllers, and can be powered by a 12-V DC source. This valve requiresonly a 100 ms current pulse for closing or opening, thus saving a significant amount ofpower when compared to the conventional solenoid valves used for irrigation control. Anaudible alarm (CEP-2242, Cui, Inc., Beaverton, OR) warns the user of possible out-of-rangereadings from the temperature or SWP sensors. When the alarm occurs, downloading ofcontroller data is recommended to identify the sensor(s) that is out of range.

The controller stores data from the sensors and some program variables in a 32-kB serialEEPROM (24LC256, Microchip Tech., Inc., Chandler, AZ). The non-volatile EEPROMretains the contents of memory until it is overwritten. By default, the controller is pro-grammed to write 10 bytes of data to the EEPROM every 15 min, providing 33 days of datastorage before any data are overwritten.

A 5-W solar panel and a 1.2-Ah, 12-V sealed lead acid (SLA) battery were initiallyselected to power the irrigation controller during system testing. The current drawn by the


irrigation controller when running (sensor readings, data storage, etc.) and when in stand-bymode is 14 and 10 mA, respectively.

A battery charge regulator is incorporated into the controller circuit, with the MCUmanaging the battery charging process. The circuit is designed to limit the initial chargingcurrent to 0.2 times the battery capacity (240 mA for a 1.2-Ah battery) while applyinga constant voltage of 2.45 V/cell to the battery (14.7 V for a 12-V battery). The battery ischarged by the solar panel until the charging current drops to less than 0.01 times the batterycapacity (12 mA for the 1.2-Ah battery). After the battery is charged, the microcontrollerswitches the charging mode to a float voltage holding the battery across a constant voltagesource of 2.25–2.30 V/cell (13.50–13.80 V for a 12-V battery). The irrigation controlleruses a precision high-side current-sense amplifier (MAX471, Maxim Integrated Products,Inc., Sunnyvale, CA) to sense the charging current.

2.2. Controller software

The irrigation controller program was written in PBASIC, a proprietary language formicrocontrollers manufactured by Parallax, Inc. The program has 480 lines of code anduses the total memory capacity of the microcontroller. The program flow diagram is shownin Fig. 5. The program contains a main loop and several subroutines that enable the MCUto perform the following tasks:

- measure the soil temperature, the SWP, and the hydraulic system pressure;- compare SWP and pipeline pressure values to threshold values programmed by the user

and downloaded into EEPROM, and make a decision about opening or closing the solenoidvalve;

- store date, time, soil temperature, SWP, system pressure, and valve status in the controllerEEPROM;

- warn the user about sensor out-of-range readings by activating an audible alarm;- when queried by the user via the asynchronous serial port, transfer data stored in the

EEPROM to a remote device;- allow the user to change the time when irrigation is allowed regardless of the priority in

irrigation schedule, the MAD, the system pressure, or the controller elevation;- manage the battery charging process.

3. Irrigation controller testing

The irrigation controller performance was evaluated during the summer of 2002 at theDepartment of Biosystems Engineering and Environmental Science, The University ofTennessee, Knoxville. To verify adequate operation of the system hardware and software,and the control of the SWP in the root zone on a real-time basis, four controllers were testedto simulate a site-specific irrigation system with four management units. Three plasticcontainers (diameter 0.5 m, depth 0.7 m) each filled with soil and cultivated with Bermudagrass (Cynodon dactylon) were used to represent an irrigation management unit. A 0.1-mlayer of gravel was placed at the bottom of each container, and the container was filled with


Fig. 5. Flow diagram of controller program for distributed control of simulated site-specific irrigation.


soil (Sequatchie Silt Loam) from that level to the top. A hole was drilled at the bottom ofeach container for the collection of drainage water if overirrigation occurred.

The SWP in the root zone was monitored using one Watermark® sensor installed at the0.2 m depth in each container. A thermistor installed in one of the containers at the samedepth as the Watermark® sensor was used to monitor the soil temperature. Each containerwas irrigated by one drip emitter with a nominal flow rate of 2.0 L h−1, at an operatingpressure of 100 kPa. A pressure transducer was installed in the mainline, immediately beforethe flow control valve. A centrifugal pump, activated by a pressure switch was used tocontinuously pressurize the system.

The SWP threshold (MAD) was set to−15 kPa, because Watermark® sensors typicallydo not produce reliable measurements for SWP less negative than−10 kPa (Shock et al.,1998). The irrigation line pressure threshold (MAD) was set at 100 kPa to match the emit-ter operating pressure requirement. The SWP in the containers was also monitored usingtensiometers to verify the proper functioning of the irrigation controllers. Two tensiome-ters were installed in each container, at the 0.2- and 0.5-m depths. The tensiometers wereequipped with pressure transducers (Motorola, Inc. MPX5700DP). The pressure transduc-ers were calibrated in the lab using the hanging water column method (Haines apparatus)(Klute, 1986). Two pressure transducers were used in the calibration procedure, with thewater column ranging from 0 to 2.75 m (0–30 kPa).

4. Results and discussion

The performance of the irrigation controller was analyzed in terms of satisfactory oper-ation of the controller hardware and software, and the control of the SWP in the root zoneon a real-time basis. The irrigation controller performance was considered satisfactory ifthe SWP in the root zone was maintained less negative than the MAD threshold plus 20%tolerance (−18 kPa) for at least 90% of the time, and overirrigation or excess water did notaccount for more than 5% of the total irrigation depth applied.

4.1. Soil water potential control

The irrigation controller proved to be effective for controlling the SWP in the root zoneof the Bermuda grass. As an example, soil water potential changes at the 0.2-m depth,measured during calendar day 202, are shown inFig. 6. Soil water potential readings werealmost constant during the night and morning. The Watermark® sensors responded well tosoil water extraction during the period of peak water use (12:00–18:00 h). Irrigation wastriggered at 18:30 h when sensors 2 and 3 indicated SWP values of−15 and−20 kPa,respectively, and irrigation was stopped at 17:30 h when sensors 1 and 2 indicated SWPvalues of−11 and−13 kPa, respectively.

The Watermark® sensors showed quick response to the wetting front, preventing overirrigation. These results confirmed preliminary tests performed in the laboratory, whichshowed that when a wetting front reaches the sensor it takes only from 5 to 20 minfor the SWP reading to change from an initial value of−25 to −10 kPa. In general,the readings of the Watermark® and tensiometer at 2.0-m depth compared well except


Fig. 6. Soil water potential (SWP) measured at the 0.20 m depth on calendar day 202.

from day 227 to 240 when the tensiometer showed a different wetting and drying trend.These differences in readings between the Watermark® and tensiometer are due to intrinsiccharacteristics of the sensors, such as response to drying and wetting, and sensor calibra-tions. However, Watermark® readings are generally considered to be more accurate thantensiometers.

The changes in SWP measured in the soil containers for 47 days are shown inFig. 7.The Watermark® measurements are presented as median readings, while those of the ten-siometers installed to monitor the performance of the Watermark® sensors are presented asthe average of the three sensor readings. The irrigation controller worked as expected overthe entire testing period, maintaining the SWP in the root zone less negative than−18 kPafor 100% of the time according to the Watermark® sensor readings. Tensiometer readingsat the 0.2-m depth confirmed adequate control of the SWP by the irrigation controller. Thetensiometer readings indicated that the SWP at the 0.2-m depth remained less negativethan−18 kPa for 98% of the time during the testing period. Tensiometers readings oftenbegan to decrease sooner than the Watermark® sensors in response to soil water extraction,and reached more negative SWP values before irrigation began. Watermark® sensors on theother hand showed very good response to wetting. Similar results were reported byMeron et


Fig. 7. Soil water potential (SWP) measured in containers irrigated with the irrigation controller during 47 days.

al. (1996), who used tensiometers to monitor Watermark®-based irrigation control systemsin a field study with cotton.

4.2. System hardware and software performance

The irrigation control system hardware and software performed all tasks as designed.Data downloaded from the controllers showed that the irrigation control system continuallymeasured soil temperature, SWP, and hydraulic pressure, and opened or closed the solenoidvalve when needed without failure. User interactions with the controllers using a notebookcomputer were also successful.

4.3. Irrigation controller cost

Hardware construction cost for one irrigation controller unit was approximately US$190 (based on January 2003 prices), including the solar panel and the battery. With sensorsand the latching solenoid valve, the total cost was US$ 310. For a production scale of 1000units, the estimated unit cost would be US$ 120 for the irrigation controller, and US$ 210including sensors and valve. These costs do not include user interface, labor, profits, anddevelopment of the controller.


Although the cost of multiple control units required by a distributed control system couldbe greater than the cost of a central controller required by a centralized control system, thewiring costs for the distributed control system would be significantly reduced. Since thedistributed controllers are in close proximity to the sensors and valves of each irrigated zone,shorter and smaller gage cables are required compared to a centralized control system.

Besides lower wiring costs, a wireless distributed control system is simpler and moreflexible than a hard-wired centralized control system. The risk of system failure causedby mechanical damage or electrical storms is minimized for a wireless distributed controlsystem. Other advantages of a wireless distributed control system are that adding a newirrigated unit to the system is simple and does not require changing the control systemprogram, adding new hardware to a central control, or burying additional cable, all ofwhich are required for modifying a hard-wired system with centralized control.

4.4. Power supply system

Battery voltage measurements recorded during the period of study showed that solarradiation values of 2.8 kWh m−2 day−1 were sufficient to maintain the 1.2-Ah battery fullycharged using the 5-W solar panel (Miranda, 2003). Considering that the average solarradiation for Knoxville, TN (latitude 36◦88′N) is 4.5 kWh m−2 day−1, a smaller solar panelwould be sufficient to power the irrigation controller during the normal irrigation season.

Fig. 8. Battery voltage and daily solar radiation observed in February in Knoxville, TN.


Table 1Available battery capacities and expected irrigation controller operation without battery recharge

Battery capacity (Ah) Number of days of operation without rechargea

1.2 2.92.2 5.32.8 6.83.4 8.2

a Number of days = (battery capacity× efficiency)/daily load.

On the other hand, the 1.2-Ah battery was completely discharged after more than 2days when the solar radiation was lower than 1.4 kWh m−2 day−1 (Fig. 8). This showsthat the battery capacity chosen was under-designed for the region. The performance ofthe controller power supply system could be improved by using a battery with a largerstorage capacity, and the solar panel power rating could be reduced. An alternative rec-ommendation to reserve the battery for continuous controller operation would be to use abattery charger controller separate from the MCU, allowing the MCU to sleep until a timerwakeup.

The battery storage capacity should be chosen considering the expected number of con-secutive days in the region with significantly reduced solar radiation. For example, thecommercially available sealed lead acid battery capacities and expected number of daysthat the irrigation controller could operate without external charging at the study site areshown inTable 1. Calculations were done assuming a daily load consumed by the irrigationcontroller of 0.29 Ah day−1 and a battery efficiency of 70%. For Knoxville, TN, a minimumbattery capacity of 2.2 Ah would be required to operate the controller without interruptionduring the period of lowest solar radiation (winter).

5. Conclusions

An autonomous feedback distributed irrigation control system for site-specific manage-ment of fixed irrigation systems was developed and tested. The DIC system proved to bereliable, affordable, and effective in maintaining the SWP in the root zone close to a pre-set value without hard-wire connections between irrigation management units. The systemmaintained the SWP in the root zone less negative than−18 kPa (the threshold value orMAD) for 100% of the time during the study. The components to assemble each individ-ual control station were purchased for approximately US$ 310 (January 2003 prices); theestimated cost of components for each unit if 1000 units are produced is US$ 210.

Acknowledgments

The authors would like to thank the Tennessee Agricultural Experiment Station, theBrazilian Agricultural Research Corporation—Embrapa, and the Brazilian National Councilfor Scientific and Technological Development—CNPq, for their financial assistance of thisresearch.


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