IoT-based smart irrigation management system using real-time data

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 7078∼7088
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp7078-7088 ❒ 7078
IoT-based smart irrigation management system using
real-time data
Asmae Hafian, Mohammed Benbrahim, Mohammed Nabil Kabbaj
LIMAS Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Article Info
Article history:
Received Jan 7, 2023
Revised Mar 15, 2023
Accepted Apr 3, 2023
Keywords:
Internet of things
Irrigation management
Irrigation requirement
Smart agriculture
Smart irrigation
ABSTRACT
An adequate water supply is essential for the growth and development of crops.
When rainfall is insufficient, irrigation is necessary to meet crop water needs. It
is a crucial and strategic aspect of economic and social development. To combat
climate change, there is a need to adopt irrigation management techniques that
increase and stabilize agricultural production while saving water, using intelli-
gent agricultural water technologies. Internet of things (IoT) based technologies
can achieve optimal use of water resources. This article introduces a smart real-
time irrigation management system based on the internet of things. It provides
optimal management of irrigation decisions using real-time weather and soil
moisture data, as well as data from precipitation forecasts. The proposed algo-
rithm is developed in real-time based on the IoT, enabling us to guide irrigation
and control the amount of water in agricultural applications. The system uses
real-time data analysis of climate, soil, and crop data to provide flexible plan-
ning of the irrigation system’s use. A case study from the Fez-Meknes region in
Morocco is presented to demonstrate the proposed system’s effectiveness.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Asmae Hafian
LIMAS Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University
B.P. 1796 Fès-Atlas 30003, Morocco
Email: asmae.hafian@usmba.ac.ma
1. INTRODUCTION
One of the biggest challenges facing humans today is climate change. This change is caused by
an increase in the earth’s temperature at an unprecedented rate. It has had significant negative impacts on
many countries around the world, particularly in developing countries that lack sufficient resources. It leads
to an environmental shortage of water and exacerbates desertification [1]. Whereas, in the agricultural sector,
85% of freshwater resources are consumed worldwide, and this percentage is expected to increase further due
to population growth and increased food demand [2]. As a result, there is a need to design more efficient
technologies to ensure the optimal use of water resources for irrigation and adaptive management to changing
environmental conditions.
In the absence of smart and cost-effective irrigation systems, developing countries consume more
water than developed countries to achieve the same yield [3]. Smart water management for irrigation is essential
to increase crop yields, reduce costs, and promote environmental sustainability [4] through effective monitoring
and optimal control. This irrigation management focuses on the efficient use of water for each plant, precisely
when and where it is required, and in appropriate quantities to offset water loss due to evapotranspiration,
erosion, or deep percolation [5].
Journal homepage: http://ijece.iaescore.com

Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 7079
In the agricultural sector, internet of things (IoT) solutions take the form of sensors connected to the
internet to collect measurement data. Their deployment allows informed decisions to be made. Using new
IoT tools, these systems can continuously monitor soil and crop water needs in real-time from anywhere via a
network of wireless sensor nodes while processing the collected data and storing it in cloud-based applications
[4]. These systems also allow farmers to choose between manual and automated options to take the necessary
actions based on the collected data.
Several authors have analyzed the advantages of these intelligent irrigation management systems in
the agricultural sector, such as [6], who reviewed the use of wireless sensor networks for irrigation purposes.
This application aims to manage and control irrigation systems to promote the effective and sensible utilization
of water resources. Nam et al. [7] discussed wireless sensor networks that use information and communication
technologies (ICT) for the management of irrigation facilities based on radio frequency. Goap et al. [3] used
the detection of soil parameters and meteorological data for the prediction of irrigation needs. Based on a
database, Munir et al. [8] proposed a smart approach for plant irrigation based on daily water needs. To
estimate the depth of water absorption, Liao et al. [9] designed an irrigation system based on real-time soil
moisture data. By utilizing long-range wide-area networks (LoRaWAN) to connect devices, it is now possible
to create IoT and cloud computing systems that are specifically tailored for use in agriculture. In this sense,
Sanchez-Sutil and Cano-Ortega [10] has developed a control and monitoring system for irrigation systems that
utilize LoRaWAN technology.
The optimal operation of an irrigation system requires the efficient development of control algorithms
that allow flexible planning of the system’s use according to the water needs. In this sense, knowledge of
several pieces of information is necessary. Some information is acquired in real-time from a network of sensors
(humidity, temperature, flow rate, and water level), while other information is fixed in advance (nature of the
soil, culture, and network of conducts). We propose an intelligent system with an architecture based on wireless
communication at all levels of the system using LoRa and Wi-Fi protocols. We have developed an IoT-based
architecture to deploy smart irrigation management applications based on the analysis of real-time data such as
climate data, soil data, and crop data, as well as the precipitation forecast for the day. It requires great flexibility
to accommodate a range of deployment configurations that include a diverse mix of technologies.
The paper is structured as follows: section 1 provides an introduction. Section 2 discusses the pro-
posed system and the methodology for irrigation management. A case study is described and explained in
section 3. Finally, the conclusion is given in section 4.
2. MATERIALS AND METHODS
2.1. System design
The proposed system for irrigation management is illustrated in Figure 1, and it consists of four parts:
IoT nodes, gateway, the things network server (TTN), and application server. The IoT nodes are wireless nodes
that integrate weather stations, soil moisture sensors, and a pump. Their primary role is to collect data from the
sensor layer elements and exchanging information with the gateway. This information is concentrated on the
TTN server to exchange data with various IoT nodes. Message queuing telemetry transport (MQTT) is used
to exchange messages between the cloud and the devices of the networks, which, in turn, communicate with
the gateway [11], [12]. The cloud server is mainly responsible for storing data and implementing the MQTT
interface for the LoRa server and application. The application server is used to get the status of devices so that
the irrigation system can be controlled and supervised. The IoT nodes used in the irrigation control system
consist of:
− Vantage Pro2 pro weather station [13], which is a station comprising an integrated suite of weather sensors
and a data visualization console within a free space radio range of 300 m. It can measure wind (speed and
direction), precipitation, temperature, humidity, atmospheric pressure, and solar radiation. The station is
connected to a local server to publish and process local data using a Raspberry Pi.
− Decagon ECH2O EC-5 soil moisture sensor that measures and monitors soil moisture to determine when
to irrigate and how much water to apply. The EC-5 uses capacitance to measure the apparent dielectric
constant of the surrounding medium [14] to determine volumetric water content. Its 70 MHz frequency
minimizes salinity and textural effects, making this sensor accurate in almost any soil or above-ground
environment. Sensor data is collected using a microcontroller board based on the ATmega2560, represented
by an Arduino ADK.
IoT-based smart irrigation management system using real-time data (Asmae Hafian)

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− Third node is the pump, which is controlled to water the field accordingly. This node is in serial communi-
cation with a microcontroller board to control the pump.
Figure 1. Proposed smart irrigation system architecture
Each node is connected in series with a WisNode LoRa module, which is an Arduino Uno format
development board. The end device (WisNode LoRa) and the main gateway, responsible for communication
with the cloud, are two necessary components at the level of LoRa communication. The main gateway is
a multi-channel LoRaWAN [15] module responsible for managing upstream and downstream messages sent
in both directions between the IoT and TTN nodes. The TTN service is specifically designed to work with
LoRaWAN TTN [10]. It supports upstream and downstream messages to IoT nodes. From TTN, information
is sent and received to the various IoT services via the available integrations. In this case, Node-Red was used,
for which an algorithm for real-time irrigation management was developed. The information generated by the
IoT nodes algorithm is stored in a DB browser for the SQLite database.
2.2. Methodology
Irrigation water management requires using the best estimate that current technology can provide to
determine crop water use and field irrigation water requirements. This section provides a methodology to
determine crop water needs,which includes the evaluation of crop water use, crop coefficients, climatic data,
reference crop evapotranspiration, effective rainfall, irrigation efficiency, dose, and duration required to provide
the necessary irrigation.
The crop irrigation needs refer to the amount of water and timing required to compensate for soil
water deficits during the growing season for a particular crop. It mainly varies depending on climate factors
(temperature, wind, humidity, solar radiation), soil factors (water table depth, soil moisture availability, texture,
conductivity), crop factors (crop type, length of crop growing season, growth stage, rooting characteristics),
and water factors (frequency of irrigation and quality of irrigation water). It can be expressed as (1) [16]:
IRn = ETc − EP − GW − ∆SW (1)
where IRn is the net irrigation requirements (mm day−1
), ETc is the crop evapotranspiration (mm day−1
),
EP is the effective precipitation (mm day−1
), GW is the groundwater contribution (mm day−1
), and ∆SW is
the soil water depleted during the season (mm day−1
). Irrigation planning requires knowledge of the frequency
and amount of rainfall. Effective precipitation is the fraction of precipitation that is available to the plant, and
not all precipitation is effective, as some are lost by runoff, deep percolation, or evaporation. The method pro-
posed by United State Department of Agriculture Soil Conservation Service (USDA-SCS) to estimate effective
precipitation is [17]:
EP = P(125 − 0.2P)/125 if P ≤ 250
EP = 125 + 0.1P if P > 250
(2)
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 7078-7088

where EP is the effective precipitation (mm day−1
), and P is the precipitation (mm day−1
).
The total amount of water lost through transpiration by the crop and evaporation from the soil surface
is known as crop evapotranspiration (ETc), which depends on the plant species, climate, and growth stage. It
can be expressed as (3) [16]–[18]:
ETc = Kc · ETo (3)
where ETc is the crop evapotranspiration (mm day−1
), Kc is the crop coefficient, which is used to relate ETo
to crop evapotranspiration ETc and depends on the crop characteristics, the time of planting or sowing, and the
stages of crop development and ETo is the reference evapotranspiration(mm day−1
).
Evapotranspiration (ETo) is a climate parameter that represents the amount of water transferred to
the atmosphere by evaporation at ground level and by transpiration from plants. It can be calculated from
meteorological data [16], but crop properties and soil factors do not influence transpiration because water is
abundantly available at the reference evapotranspiration surface. ETo can be estimated using various methods,
including empirical and scientifically derived methods. The appropriate method selection depends on the type,
accuracy, and duration of available climatic data [19]. Less precise reference evapotranspiration methods may
provide adequate precision for long-term estimates. However, complex equations are needed for short-term
estimates. The Penman-Monteith method is recommended as the only method for determining ETo. This
method is more reliable for any longer period and works well for daily calculations and estimating monthly
or seasonal water needs. It requires meteorological data on radiation, air temperature and humidity, and wind
speed. Sometimes complete meteorological data is not available or not reliable for estimating transpiration
using the Penman-Monteith method. In such cases, Food and Agriculture Organization of the United Nations
(FAO) [20] recommends the Blaney-Criddle, Radiation, Modified Penman, and Pan Evaporation method as
empirical methods for estimating ETo under different climatic conditions.
In our proposed system, irrigation management requires real-time data. Thus, we computed the ref-
erence evapotranspiration in two cases: when meteorological data is available and when it is missing, using
both Penman-Monteith and Blaney-Criddle methods. ETo can be expressed as follows using the FAO Penman-
Monteith method [20]:
ETo(P M) =
0.408 δ(Rn − G) + Γ 900
T +273 u2(vs − va)
δ + Γ (1 + 0.34 u2)
(4)
where ETo(P M) is the reference evapotranspiration with the Penman-Monteith method (mm day−1
), Rn is the
net radiation at the crop surface (MJ m−2
day −1
), G is the soil heat flux density (MJ m−2
day −1
), δ is the
slope vapor pressure curve (kPa ◦
C−1
), Γ is the psychrometric constant (kPa ◦
C−1
), T is the mean daily air
temperature at 2 m height (◦
C), u2 is the wind speed at 2 m height (m s−1
), vs is the saturation vapor pressure
(kPa), va is the actual vapor pressure (kPa), and (vs − va) is the saturation vapor pressure deficit (kPa). The
Blaney–Criddle method to estimate ETo can be expressed as (5) [21], [22]:
ETo(BC) = p(0.46 × Tmean + 8.13) (5)
where ETo(BC) is the reference evapotranspiration with Blaney-Criddle method (mm day−1
), p is the mean
annual percentage of daytime hours, and Tmean is the mean air temperature at 2 m height (◦
C). To obtain the
total water needs, irrigation losses are estimated. It is done by evaluating the total water requirement (IR)
during the time stage, which is given by (6) [16]–[20]:
IR =
IRn
Ie
(6)
where IR is the total water requirement (mm day−1
), IRn is the net irrigation requirements (mm day−1
), and
Ie is the irrigation efficiency. The irrigation efficiency depends on various factors, including the transpiration
relationship, the sprinkler factor, the uniformity coefficient, and the salinity of the irrigation water.
The next step is to determine the dose of watering, which is the amount of water brought to the crop
during irrigation that fills the soil reservoir up to the field capacity. It is expressed as (7):
Dw =
H(Cc − Pm)f
Ie
(7)

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where Dw is the total irrigation dose (mm), H is the root depth (cm), Cc is the field capacity (mm/cm), Pm
is the wilting point (mm/cm), f is the water availability in the soil, and Ie is the irrigation efficiency. The
irrigation duration (ID) is the time required to provide the necessary irrigation dose. It depends on the flow
rate (l/s), the amount of watering required (mm), and the area of the field to be irrigated (ha) and can be
represented by (8):
ID =
2.78.Dw.Si
D
(8)
where ID is the irrigation duration (hrs), Dw is the total irrigation dose (mm), Si is the area of the field to be
irrigated (ha), and D is the flow rate (l/s).
Properly irrigating a crop requires making complex and frequent decisions that must be adjusted to the
variability of water needs based on the climate and the plant’s growth. This complexity can cause water stress
due to a lack of water or air if too much water is added. Providing the plant with only the required amount of
water at the appropriate time leads to increased plant vigor, optimal crop yields, better crop quality, plant resis-
tance to disease, greater water utilization, and reduced irrigation costs. To enhance irrigation decision-making,
Figure 2 illustrates a flowchart of the proposed real-time irrigation management and scheduling algorithm. The
system executes this multi-step set of functions according to algorithm 1.
Figure 2. Flowchart diagram of the proposed architecture for smart irrigation management

Algorithm 1 Algorithm of irrigation management
Data: RH, T , u2, Ra, bar, dailyR for the climatic data, daily weather forecast, Kc for the crop, soil humidity for the soil data, and dripper data;
Result: irrigation requirement IR, irrigation dose Dw, and irrigation duration ID;
Initialization;
if weather Station available then
retrieve data from weather station;
calculate ETo; {Penman-Monteith method}
calculate EP ;
deduction of IR;
else
retrieve data from API;
calculate ETo; {Blaney-Criddle method}
calculate EP ;
deduction of IR;
end
if rain probability > threshold then
if soil moisture <= SMmin then
calculate net irrigation requirement;
else
go back;
end
else
go back ;
end
Update the irrigation requirement IR;
if IR > 0 then
calculate Dw;
calculate ID;
irrigation process;
else
go back to the beginning of current section; {retrieve new data}
end
Update database;
When initiating the system, it requires three types of data:
− Soil and crop characteristics extracted from a database;
− Real-time weather and soil moisture data;
− Daily rainfall forecast data.
After estimating the gross irrigation water requirements (IR), the system compares the received prob-
ability of rainfall with the minimum rainfall threshold that has been set. When there is a greater chance of
rainfall, the system compares the humidity level to the specified minimum level (SMmin), which is controlled
in the water comfort zone of the plant, between the field capacity and wilting point. This quantity varies de-
pending on the type of soil. For loamy soils, they can absorb a greater amount of available water. The irrigation
process should begin before the available water loss in the soil reaches (SMmin). If the rate is lower, the sys-
tem updates the irrigation water requirements to minimize water expenditure. A crop’s irrigation water needs
depend on the rainfall and the soil’s ability to absorb and return water. To proceed with the irrigation process,
the system checks if the difference between crop evapotranspiration (ETc) and effective precipitation (EP) is
greater than 0 (IR > 0). If not, it is not necessary to irrigate.
The proposed system operates in two modes: automatic and manual. In manual mode, the user de-
cides based on information about soil moisture and predicted rainfall. In automatic mode, the user defines the
initialization parameters (station, soil, and crop), and the system automatically programs when the plant needs
to be irrigated. Additionally, the system can handle changes in predicted precipitation values.
3. RESULTS AND DISCUSSION
This section presents the tests carried out to validate the algorithm and the system proposed in this
research. Figure 1 highlights a case study of the proposed IoT system in which a prototype was implemented
for tomato cultivation in the Fez-Meknes region of Morocco, situated at latitude 33°53’36” North and longitude
5°32’50” West. Data was collected in real-time every 15 minutes from April until August. The crop coefficient
Kc for the tomato [23] used in the analysis is represented in Figure 3. According to [24], the soil texture in this
region is loamy-clay.

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Figure 3. Crop coefficient Kc variation for tomato during the growing season
Climate and moisture data are collected in real-time as shown in Figure 4, with a daily time step (ev-
ery 15 minutes), by the Vantage Pro2 pro weather station in Figure 4(a) and soil moisture, by the Decagon’s
ECH2O EC-5 device as shown in Figure 4(b). Measurements of the weather station should be made 2 m above
a large area of green grass. For data input, Table 1 contains climate data from the weather station. Average
daily values over the decade of air humidity RH, air temperature T, wind speed u2, solar radiation Ra, pres-
sure atmospheric bar, and daily precipitation dailyR. Since climate data is not always available or unreliable,
we used an online service, OpenWeatherMap, via an API.
(a) (b)
Figure 4. Data extracted from (a) the Vantage Pro2 pro weather station and (b) Decagon’s ECH2O EC-5 soil
moisture
For soil moisture measurement, we installed Decagon’s ECH2O EC-5 device in the prototype.
Figure 5 represents the variation of the soil moisture sensor measured during the time phase. The pump was
also installed in the area for irrigation management. We used node-RED [25] as the application for which we
developed our real-time irrigation management algorithm as shown in Figure 6. As mentioned previously, to

calculate the actual daily evapotranspiration, we based ourselves on the two cases (presence and absence of
the weather station). For this, we used the two FAO methods: Penman-Monteith and Blaney-Criddle. Figure
7 represents the variation of ETo during the time step with the Penman-Monteith method in Figure 7(a) and
the Blaney-Criddle method in Figure 7(b). The collected data was analyzed and processed using the proposed
algorithm in Figure 2. Table 2 shows the results, including reference evapotranspiration (ETo), crop evapo-
transpiration (ETc), net irrigation requirements (IRnet), total irrigation requirements (IR), and total irrigation
dose (Dw).
Our system aims to achieve intelligent irrigation management, which aims to significantly reduce
water consumption and maintain the appearance of green spaces compared to the projected green spaces based
on real-time weather and soil data, as well as daily rainfall forecast data. The obtained results reveal that using
an intelligent approach to make more accurate and smart decisions certainly helps to improve the results of an
IoT-based system. The system automatically makes irrigation decisions based on a schedule that determines
when the plant requires irrigation, the amount of water required, and the duration of irrigation. The program
also manages climate change, including predicted rainfall. Before making the decision, it takes into account the
rainfall forecasts and soil water availability, which ensures that irrigation is controlled within the water comfort
zone for the plant, avoiding water stress.
Table 1. Climatic data from weather station
Time stage i (Decades) Parameters
RH (%) T (◦C) u2 (Km h−1) Ra(Wm−2) bar (hPa) dailyR (mm)
April 1 48.47 23 4 326 1017 3.02
April 2 48 20.5 5.26 350 1019 1.78
April 3 29.5 21.42 10.14 390 1016 0.65
May 1 30 25.5 6 350 1015 0
May 2 40.14 28.6 6.6 300 1015 0
May 3 50 33.4 9 564 1011 0.024
June 1 51.7 25 5.65 384 1015 0
June 2 34 34.3 9 429 1012 0.04
June 3 52.45 25.2 7.74 345 1015 0
July 1 46.76 25.26 6.6 400 1012 0
July 2 48.7 32.45 5.33 588 1014 0
July 3 28.08 34.45 5.21 640 1014 0
August 1 49.2 31.5 4.6 457.4 1012 0
August 2 49.57 30.47 4.75 746.5 1011 0
August 3 34 33.09 5 639 1012 0
Figure 5. Variation of the soil moisture during the time stage

7086 ❒ ISSN: 2088-8708
Figure 6. The GUI of the real-time irrigation management using node-RED
(a) (b)
Figure 7. Variation of ETo during the time stage with the two methods (a) Penman-Monteith and
(b) Blaney-Criddle
Table 2. Algorithm results during the time stage
Time stage i (Decades) Parameters
ETo (mm day−1) ETc (mm day−1) IRnet (mm dec−1) IR (mm dec−1) Dw (l m−2day−1)
April 1 8.05 4.025 10.2 14.2 1.4
April 2 10.95 5.47 37.05 51.45 5.15
April 3 7.06 3.53 28.8 40 4
May 1 7.5 4.27 42.57 60 5.93
May 2 9 5.13 51.3 71.25 7
May 3 8.5 4.85 48.21 67 6.7
June 1 7.8 7.3 73.32 102 10.1
June 2 12.32 11.5 114.5 159 15.8
June 3 9.5 8.93 89 124 12.41
July 1 9.2 10.58 105.8 147 14.6
July 2 12 13.8 138 192 19.1
July 3 13.3 15.3 152 212 21.24
August 1 12 12.48 124.8 173 17
August 2 12.4 12.8 127 180 17.19
August 3 11 11.44 114.4 159 15

4. CONCLUSION
This article presents an architecture for IoT-based intelligent irrigation management. This architecture
is based on an algorithm that uses real-time weather and soil moisture data, also a rainfall forecast query that
provides the ability to make the irrigation decision. The proposed system offers extensive opportunities to
enhance system functionality and adaptation to the needs of each implementation. Also, its performance can be
enhanced by incorporating new features through reprogramming. The proposed system is designed to meet the
irrigation water needs of tomatoes grown near the site in the Fez-Meknes region. Our system offers the ability
to monitor, analyze, and make irrigation decisions from anywhere while also reducing water consumption by
accurately determining when and how much water the plant requires.
ACKNOWLEDGEMENTS
This work is part of the Research-Development-Innovation of the Fez-Meknes Region project in
Morocco.
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BIOGRAPHIES OF AUTHORS
Asmae Hafian received the M.S degree in Engineering of Industrial Automated Systems
from the University of Sidi Mohamed Ben Abdellah, Fez, Morocco, in 2017. She is currently a Ph.D.
student at the Faculty of Sciences Dhar El Mahraz. Her main research interests are in engineering
sciences, modeling and optimization. She can be contacted at email: asmae.hafian@usmba.ac.ma.
Mohammed Benbrahim received the B.Eng. degree in electromechanical engineering
from the Higher National School of Mines, in 1997 and the M.Sc. and Ph.D. degrees in automatic and
industrial informatics from the Mohammadia School of Engineers, in 2000 and 2007, respectively.
Currently, he is a Full Professor at the Department of Physics and the program coordinator for the
Master Smart Industry at the Faculty of Sciences, Sidi Mohamed Ben Abdellah University. His
research interests include robotics, automatic control, intelligent systems, predictive maintenance,
modeling, and optimization. He can be contacted at email: mohammed.benbrahim@usmba.ac.ma.
Mohammed Nabil Kabbaj is a Full Professor at the Faculty of Sciences, University of
Fez, where he is the Program Coordinator of Mechatronics and Embedded Systems Bachelor. His
research interests include control engineering, fault detection, and diagnosis of complex systems.
Before joining the University of Fez, he received a Ph.D. degree from the University of Perpignan
in 2004 and has been a postdoctoral researcher at LAAS-CNRS in Toulouse. He can be contacted at
email: n.kabbaj@usmba.ac.ma.

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