International Journal of
Water Resources and Environmental Engineering

  • Abbreviation: Int. J. Water Res. Environ. Eng.
  • Language: English
  • ISSN: 2141-6613
  • DOI: 10.5897/IJWREE
  • Start Year: 2009
  • Published Articles: 310

Full Length Research Paper

Prototype of a control system for trickle irrigation systems considering distributors’ flow rate variation

Elnemr Moataz
  • Elnemr Moataz
  • Department of Agricultural Engineering, Faculty of Agriculture, Damietta University, Damietta Egypt.
  • Google Scholar

  •  Received: 05 February 2019
  •  Accepted: 12 March 2019
  •  Published: 31 May 2019


A prototype of a real-time closed loop automatic control system has been successfully developed to suit trickle irrigation systems management. The control system was designed to manage the irrigation system based on collecting data through soil moisture sensors located at three different locations acting each third of the lateral to put the effect of emitters’ flow rate variation along laterals on soil moisture content distribution in consideration. Soil moisture content which will be used to start and stop the irrigation process will be based on the average of three sensors’ readings. Required inputs to manage the irrigation system were soil field capacity (FC), permanent wilting point (WP), and percentage of moisture allowed depletion (MAD). Irrigation process will begin when the soil moisture content equals the desired percentage of available water and it will stop after reaching soil field capacity. The system was provided with ability to check some defects that are related to the irrigation system like clogging, poor uniformity, and water pumping failure. Laboratory assessment showed that the control system has applied the design considerations and is expected to save irrigation time if implemented with low or poor uniformity trickle irrigation networks when compared to high uniformity networks. The study recommended evaluating the effect of this control system implementation in field especially with trickle irrigation networks which show poor uniformity to use the potentials of the control system to predict emitter clogging, and poor uniformity which are mostly appear in such irrigation systems.

Key words: Automatic control, closed loop, flow rate variation, modern irrigation systems, real time.



Due to the ability of modern irrigation systems in saving water, replacement of traditional irrigation systems with the modern systems tends to be a must especially in arid and semi-arid areas (Douh and Boujelben, 2011). This replacement may lead to excessive consumption of energy than traditional irrigation systems as they are the main element of energy consumption in agriculture (Hatirli et al., 2006). Trickle irrigation systems are considered water and energy-saving systems if compared to   other   modern    systems    like    sprinkler   irrigation. Automatic control as a modern technology can help trickle systems operators to face the challenge of achieving successful system management which will lead to efficient use of water and energy (Navarro-Hellín et al., 2016; Puerto et al., 2013). Turning irrigation systems into intelligent systems can help in saving water up to 30% if compared to conventional irrigation (Dassanayake et al., 2009). Automatic control systems are mainly relying on collecting climatic and/or soil data and analyzing them to predict the suitable  irrigation  scheduling according to the fed model or algorithm. Soil moisture is considered by many authors the key to successive scheduling (Cardenas-Lailhacar and Dukes, 2010; Soulis et al., 2015). Virtual sensing technique has become a very efficient and powerful tool to support the control techniques (Ponsart et al., 2010). Irrigation control systems that are based on  moisture sensing devices are more recommended technique. Moisture sensing devices are useful tools for collecting the required moisture data in the root zone considering the effect of water consumption by plants, soil properties and climate effect (Vereecken et al., 2008). Closed-loop control system has increasing interest and wide implementation in irrigation systems during the last years. The studies of Kim et al. (2008, 2009), Li et al. (2011), Constantinos et al. (2011) and Pfitscher et al. (2012) indicated the importance of closed-loop control systems based on on-line soil moisture monitoring for reaching efficient use of water and energy. It has been remarked that all of these studies tend to narrow the gap between the management scenarios that will be implemented by the control system and the real water needs for plants. Also, these studies depended on collecting moisture data from one certain point or several points through the field without considering   any   common   problems   that  accompany system operations like distributors clogging or poor hydraulic performance. Irrigation system operation if based on the information collected from one point along lateral will face the problem of the possible significant variation between distributors’ application rates which may lead if neglected to over and/or deficit irrigation as a result of the deceptive measurement of soil moisture content. The objectives of this work are as follows:-

1.  Develop a prototype of a real-time, closed-loop control system based on soil moisture monitoring to manage trickle irrigation systems considering distributors’ flow rate variation along lateral. The system will be provided with monitoring features to predict some problems that may appear during irrigation system operation like clogging, poor hydraulic performance, loosing sensor’s signal, and water pumping failure.
2.  Predict the effect of using the proposed control system on operation time and thus the amount of applied water for trickle irrigation system with high or poor hydraulic performing irrigation networks.




The control system was developed by Elnemr (2017). The proposed control system is a real time closed-loop control system. The system design is mainly going to be compatible with trickle irrigation systems. Laboratory assessment has been in operation since the year 2018 in the Department of Agricultural Engineering, Damietta University-Egypt to test how the system can apply the design considerations.

General description and hardware construction

The control system appears in a containing box 400 mm height, 300 mm width, and 170 mm depth. Hardware was mainly divided into three parts which are operation panel, data processing and control sets, and three soil moisture sensing devices that will be fitted  along trickle irrigation lateral. Control system interface is shown in Figure 1. Two main paths on the lower edge are allocated for the system feeding current and pump connection cables. The other one is for sensor feeding current and signal cables. The interface included digital operation panel (Touchwin OP320-A) which is composed of digital screen in addition to numeric buttons. The operation panel was connected to the PLC to enable the user enter the required inputs to the PLC and show system operation data. Three indication lamps were fitted in the containing box each of them indicating the signal of one of three sensors.



Data processing and control sets included wire terminals, Programmable Logic Control unit (PLC, Fatek FBs-B4AD), Conductor to switch the pump ON or OFF according to the order received from the PLC, AC adaptor to reduce the 220V electric potential difference to 12V which is suitable for the PLC. A volt regulator is fed from the AC adapter and lowers the potential difference to 5V to feed the sensors with required current. Figure 2 describes the hardware components and their connection with each other.



Manufacturing data of the sensors are listed in Table 1. Each of the sensors has three connections, two of them with the electric source (Negative and positive) and the third one was for signal transfer. All these connections were made through a three-wires shielded  cable.  Analog  signals  of the sensors were transferred to the PLC to be processed according to the system software design and algorithm.
Design considerations and software preparation
Design considerations and required data processing was fed to the PLC unit through its own open source program. System software design was to apply the following considerations:-
1.  Manage the irrigation system based on soil moisture content monitoring, which is considered the crucial part of scheduling tools for irrigation management.
2.  ON/OFF decision for the irrigation system will be taken according to measuring soil moisture content by soil moisture measuring sensors which will be established at  three  points  along lateral that are in the first, middle, and last third of the lateral. The average of the three values will be considered the current soil moisture content. This is supposed to consider the variation in the distributors’ application rates which may help to avoid over and deficit irrigation.
3. The required inputs of the irrigation field area field capacity (FC), temporary wilting point (WP), and moisture allowed depletion (MAD) which is a factor that is related for both soil and crop properties as they are essential data for irrigation management practices (FAO, 1998).
4.  The control system will be able to transfer between manual or automatic modes. Manual operation mode will give the flexibility to the users to operate the system in case of sensors malfunctioning or other purposes like irrigation network testing, flushing, etc.
5. The control system will be provided by a monitoring technique to warn  the  user  about  any  possible problem whether related to the control system like moisture sensors malfunction or the irrigation system including poor uniformity conditions, and water pumping failure.
ON/OFF decision
As shown in Figure 4, the user will have the ability to choose between manual (M) or automatic mode (Au) in the beginning of operation. In case of choosing manual mode, the user will be asked to enter the required operation time (T) in hours with 1-digit accuracy. In case of Au mode, the user will be asked to enter the values of FC, WP, and MAD. All these values will be fed to the system as percentages with two-digit accuracy. System will automatically check the sensors signals. MC value will be considered as the average of the three readings of the sensors to put the emitters’ flow rate variation and its impact on soil moisture distribution along lateral into consideration. In case of losing one or two signals from the sensors, the system will calculate MC as the average of the rest signals. This means when the system gets just two signals, the system will change the way of calculating MC to be the average of these two signals. In case of receiving just one signal, it will prompt the MC directly to calculate the average of soil moisture content (MC). MCon, the MC that will prompt the decision of starting the pump will be calculated according to the following equation:

The control system  was designed to start the pump after consuming the allowed depletion of available water and stop after reaching the FC.

Monitoring and checking system technique

The control system was provided with checking sensors signal as earlier clarified. This feature will enable the system to keep the irrigation process running with minimal errors in moisture content calculation due to exclusion of the zero values received by the damaged sensor(s). If three signals are lost, the system will turn automatically to the manual mode. When losing any of the sensors’ signals, the connected lamp will flash to clarify the responsible sensor for the error. The control system will record the increase in moisture content based on the received signals from each sensor after starting the pump for 15 min. If there is no increase in soil moisture content at any of the sensors, the sensor error lamp will light. If the soil moisture reading is stable for the three sensors together, then there is a high possibility that the pump is not working properly. The control system will hint the user by “pump error” message on the digital screen. Also, the system will monitor the variation of the increase in soil moisture between the three points. If the variation between soil moisture content of the three points exceeds 25%,  the control system will assume that the system is working in unacceptable level of uniformity and will alarm the user by “system error” message. The value ‘25%’ was cited from Anyoji (1993) who clarified that flow rate variation at this percentage may mean that the system is working under fully turbulent flow conditions and emitter exponent equals 1. In cases of pumping or system errors, the control system will turn automatically to manual mode as shown in Figure 3. The decision of turning to the manual mode in the cases of losing all sensors’ signals, high flow rate variation, possible clogging, or pumping failure is taken by the control system to avoid over or deficit irrigation due to wrong calculations expected in case of such exceptional circumstances. This also gives the user  the  opportunity  to  take  his  maintenance procedures. The control system takes the pumping off decision when MC value equals the FC value. System interface has an alarm button (ALM) to check the system and display the existence of any of the previously mentioned errors.
Operation panel data
Functions required to be shown on the digital screen and those of numeric button are fed to the system through the open source software. Digital screen was designed to show the operation data and guide the user for the buttons he can use to transfer between operation modes, entering data, and check the system errors as shown in Figure 4. Shown operation data included calculated MC value, total irrigation time and the selected operation mode. Any errors fed back to the control system can be displayed on the digital screen by alarm button (ALM) as shown in Figure 1.
System test
Laboratory assessments are being performed to test the ability of the control system to apply the design considerations. Assessments include testing the manual mode, moisture content calculations, system error detection, and field work simulation to state how successfully the proposed control system can adjust the irrigation system operation time. Electric centrifugal water pump 0.5 hp (0.37 kW) - 220V was connected to the system to examine the accuracy of ON and OFF pumping decisions according to the control system design considerations.
Testing manual mode
The control system was adjusted to work on manual mode for 2, 10, and 20 min. The operation time of the pump was calculated to be compared to the required time.
Sensor calibration
Twelve wetted clay and sand soil samples were used in order to calibrate the sensor reading with the real soil moisture content. Gravitational method was used to determine soil moisture content. Moisture content read by the sensors was shown on the digital screen. Three soil moisture sensors were connected to the system using shielded cable in order to test how successful the response of the control system is to achieve the design considerations purposes. After recording the MC reading from the control system, soil samples were put in the steel pots, water was added to soil and the wetted sample was left for 24 h to assure the moisture homogeneity in the sample.
Data was fed to curve expert software (Version 1.34) to obtain the required formula. Weibull model resulted as follows:
S= Sensor soil moisture reading %; M= Real weight- based soil moisture content, %.
Automatic mode and system ON/OFF decision
Automatic mode was tested in order to examine the ability of the system to take the ON/OFF decision of the irrigation system according  to  the  design  considerations.  Sensors  were immersed  
one by one in three 500-ml steel pots filled with wet clay soil. Initial average water content reading of the sensors was assumed to be the wilting point and it was fed to the system to assure the response of the system to take the ON decision. FC was assumed as 40% which equaled 78.1% of sensor reading according to Equation 2. MAD value was 30%. Water was applied for the three pots gradually till the MC reading on the digital screen reaches equivalent reading for the entered value of FC. After finishing the previous test, the readings of the soil moisture content on the panel screen were recorded. Wires of the sensors were unplugged one by one to monitor the change in soil moisture content average calculation due to the change in received signals.
Predicting possibility of clogging or pumping failure
Each of the three soil moisture sensors is proposed to be fitted under one distributor of the irrigation system. If this distributor is clogged, the received reading of soil moisture will affect the accuracy of irrigation system operation decisions. In order to test the ability of the control system for predicting clogging, each sensor was connected individually to the control system and immersed vertically in a pot containing soil without adding any water to avoid the increase in moisture content for 15 min. The three sensors were connected to the system, immersed vertically in the pots and kept without  increase  in  moisture  content  for  15  min  to  examine the ability of the control system to predict possible pumping error. The test of system ability to predict clogging possibility or the high variation between distributors’ application rates was conducted by connecting just two sensors to the PLC. The two sensors read the average initial moisture content of the soil in the pots. One of the soil samples was kept dry and water has been applied slowly to the other one till increasing the MC reading by 12.5% which gives a difference in moisture content of 25% between the two soil samples.
Field work simulation
Data collected from a field experiment by El-Nemr (2013) using drip irrigation built-in emitters was used to calculate the expected variation in irrigation duration when using the control system compared to the calculated irrigation time of the mentioned study based on the average flow rate of 20 emitters. Two scenarios were simulated by selecting three sensor positions from each one-third of lateral. The first case is selecting emitters which have the nearest flow rate values to the average flow rate to act as highly uniform emitters’ flow rate distribution (Uh). The second involves selecting the farthest emitters from each one-third of the laterals to the average flow rate of emitters along the lateral to act as the poor uniformity drip irrigation networks (Ul).





Manual mode operation
The results show that the operation time of the connected pump was exactly the same input time. The system was able to stop the pump operation any time as required by the user.
Automatic mode operation
The control system is able to calculate MC based on average of the three sensors’ signals. Variation in the number of signals received from the sensors required an accurate calculation for MC by the control system according to the design procedures.
The control system started pumping directly after finishing automatic mode inputs settings. This was because the initial moisture content was 15.8% which equals the sensor reading of 76.01%. This value is less than the proposed MC to start the pump (19.6%) and equals the sensor reading of 58.88%. The system interacted accurately with the change in soil moisture signals received from the sensors. The OFF decision of the control system was when MC equaled  the  fed  value of FC.
Predicting clogging and pumping failure
The control system showed a response to the possibility of distributors clogging and pumping failure. As there was no increase in MC value for 15 min after starting the pump, the pump has been turned off and the control system showed the message “Pump error” on the digital screen” and was ready for time input in the manual mode. There was also a response for the high variation in application rates of the system and showed the message “system error”.
Field work simulation
Data listed in Table 2 show the expected variation in network operation time due to the use of the proposed control system. The two scenarios of irrigation network operation showed that using the control system is expected to increase the operation time in both Uh and Ul scenarios. The cases of signal absence showed that Uh will record an increase in operation time in the cases S1+S3, S2+S3, S2, and S3 while S1+S2 and S1 will show a decrease in operation time. The Ul network scenario showed increase in operation time in cases S2+S3, S2, and S3 while it showed a decrease in operation time in the other cases. Applying Uh and Ul scenarios revealed that the control system helped to decrease the operation time of the irrigation network for the Ul if compared to Uh in cases of receiving all signals, S1+S2, S1+S3, and S1; whereas it was higher in cases of S2+S3, S2, and S3.




Results obtained from the laboratory assessment showed that the introduced prototype has applied its design considerations successfully. Manual modes succeed in operating the pump for identical time as required. Automatic mode operation gave the ON decision according to the design calculations and gave OFF decision at the entered FC value. During the tests, the MC of ON decision was not exactly as the proposed one. This is because the MC value in the beginning of the test (15.8%) was lower than the proposed one (19.6%). Because it was not  possible to adjust the initial MC of the used soil sample, or monitor the actual moment that MC of the soil reaches the proposed MC to start the system, the control system reaction is considered acceptable and indicates the ability of the system to start the pump at the required MC value. To keep this accuracy of the control system, it is essential to recommend making calibration for the sensors when the sensors are changed or moved to the field as recommended by Girisha et al. (2012) due to the sensors’ expected poor accuracy. The ability of the control system to predict the possibility of distributors clogging or water pumping failure, besides detecting the high variation in distributors flow rates in addition to system user alert function, is considered a protective maintenance tool for both the irrigation system and the control system. These features prevent the control system from operating the system with deceptive data from the monitoring points. It also gives an indicator to the user about possible faults in his irrigation system components and operation. This helps in achieving one of the main purposes of automatic control which is saving water and energy in a better way. Field work simulation showed that the control system had an effect on operation time whether by decrease or increase. Modification in irrigation system operation time is expected because of the difference in emitters’ average flow rate of the used field data and the control system calculations considerations. Results of expected difference in operation time showed that the control system caused a decrease in operation time in case of Ul networks in 4 cases, with one of them being the default case of the system when all the signals are received. This possibly implies that the system helps in case of low uniformity networks more than the high uniformity networks for saving water, which will be followed by increasing water and energy use efficiencies. In addition to this expected feature, the ability of the control system to continuously monitor the soil moisture under distributors make it possible for the system to predict the emitters’ clogging which is one of the expected phenomena in Ul trickle irrigation systems (Yavuz et al., 2010). Also, the prediction of emitter clogging will help to lower the operation costs and increase the system efficiency (Qingsong et al., 2008). As previously discussed, a consideration of the features introduced by the proposed control system and the higher possibility of saving water in case of low uniformity trickle irrigation networks has shown that the system can be used in low uniformity networks to optimize the system ability to predict emitter clogging, pump failure, and operation time modifications in case of losing one or more sensors’ signals. 



The proposed prototype of a real-time closed loop control system has been successfully developed to control trickle irrigation system operation. The control system  was  able to take ON/OFF decisions according to the design considerations whether in automatic or manual mode. It had the flexibility to change the MC value which is used in system operation if there was any lost signal from the sensors. System monitoring features showed the required response to alert the user about possible clogging, poor uniformity, or water pumping failure. These features were considered important as a protective system tool to make any required maintenance to the sensors or the irrigation system to assure achieving the desired impact of using the developed automatic control system. Field work simulation results showed that the control system will make a variation in irrigation time whether working with high or poor uniformity of distributors’ flow rates. The control system showed lower increase in irrigation time at poor uniformity networks than the high uniformity networks. In case of losing one sensor signal or more, the control system was able to decrease operation time in most cases for poor uniformity networks. According to the obtained results, the proposed control system is able to apply the design considerations and it is recommended that the impacts of system implementation in the field especially with poor uniformity trickle irrigation systems be evaluated.




The author has not declared any conflict of interests.



Anyoji H (1993). Drip Lateral Design Based on a Maximum to Minimum Pressure Heads Ratio. Journal of Irrigation Engineering and Rural Planning. (24):32-43.


Cardenas-Lailhacar B, Dukes MD (2010). Precision of soil moisture sensor irrigation controllers under field conditions. Agricultural Water Management 97:666-672. 


Constantinos MA, Sotiris EN, Georgios CT (2011). A Smart System for Garden Watering using Wireless Sensor Networks. ACM MOBIWAC 2011, Miami, USA.


Dassanayake DK, Dassanayake H, Malano GM, Dunn Douglas P, Langford J (2009). Water saving through smarter irrigation in Australian dairy farming: use of intelligent irrigation controller and wireless sensor network. 18th World IMACS/MODSIM Congress, Cairns, Australia, pp 4409-4417.


Douh B, Boujelben A (2011). Improving water use efficiency for a sustainable productivity of agricultural systems with using subsurface drip irrigation for maize (Zea mays L.), Journal of Agricultural Science and Technology B1 (JAST):881-888.


El-Nemr MK (2013). Role of Emission Uniformity in the Sustainable Management of Drip Irrigation System. World Research Journal of Agricultural and Biosystems Engineering 2(1):17-23.


Elnemr MK (2017). Dec 24. Automatic control system for poor hydraulic performance trickle irrigation systems. Egypt patent 2166/2017.


Food and Agriculture Organization (FAO) (1998). Crop evapotranspiration-Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56.


Girisha KG, Sheng Z, Clark JA (2012). Evaluating the accuracy of soil water sensors for irrigation scheduling to conserve freshwater. Applied Water Science 2:119-125.


Hatirli SA, Ozkan B, Fert C (2006). Energy inputs and crop yield relationship in greenhouse tomato production. Renewable Energy 31:427-438.


Kim Y, Evans RG, Iversen MV (2008). Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network. IEEE Transactions on Instrumentation and Measurement 57(7):1379-1386.


Kim Y, Evans RG, Iversen MV (2009). Evaluation of Closed-Loop Site Specific Irrigation with Wireless Sensor Network. Journal of Irrigation and Drainage Engineering, ASCE, January-February 2009.


Pfitscher LL, Bernardon DP, Kopp LM, Heckler M, Behrens J, Montani PB, Thome B (2012). Automatic control of irrigation systems aiming at high energy efficiency in rice crops. Devices, Circuits and Systems (ICCDCS), 8th International Caribbean Conference pp. 1-4.


Ponsart J, Theilliol D, Aubrun C (2010). Virtual sensors design for active faulttolerant control system applied to a winding machine. Control Engineering Practice 18:1037-1044.


Puerto P, Domingo R, Torres R, Pérez-Pastor A, García-Riquelme M (2013). Remote management of deficit irrigation in almond trees based on maximum daily trunk shrinkage. Water relations and yield. Agricultural Water Management 126:33-45.


Qingsong W, Gang L, Jie L, Yusheng S, Wenchu D, Shuhuai H (2008). Evaluations of emitter clogging in drip irrigation by two-phase flow simulations and laboratory experiments. Computers and Electronics in Agriculture 63(2):294-303.


Soulis KX, Elmaloglou S, Dercas N (2015). Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems. Agricultural Water Management 148:258-268. 


Vereecken H, Huisman J, Bogena H, Vanderborght J, Vrugt J, Hopmans J (2008). On the value of soil moisture measurements in vadose zone hydrology: a review. Water Resources Research 44:W00D06.


Yavuz MY, Demırel K, Erken O, Bahar E, Devecıler M (2010). Emitter clogging and effects on drip irrigation systems performances. African Journal of Agricultural Research 5(7):532-538.