Smart Irrigation System for Water Management in Agriculture

Results in Engineering 22 (2024) 102283
Available online 21 May 2024
2590-1230/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Enhancing water management in smart agriculture: A cloud and IoT-Based
smart irrigation system
Bouali Et-taibi
a , d , *
, Mohamed Riduan Abid
b
, El-Mahjoub Boufounas
a
, Abdennabi Morchid
c
,
Safae Bourhnane
d
, Tareq Abu Hamed
e
, Driss Benhaddou
f , g
a
REIPT Laboratory, Faculty of Sciences and Technology, BP 509, Boutalamine, Errachidia, Moulay Ismail University of Meknes, Morocco
b
TSYS School of Computer Science, Columbus State University, GA, 31907, USA
c
Department of Physics, Laboratory of Informatics, Signals, Automation and Cognitivism (LISAC), Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah
University, BP 1796, Fes-Atlas, 30003, Fez, Morocco
d
School of Science and Engineering, Al Akhawayn University in Ifrane, PO Box 104, Hassan II Avenue, 53000, Ifrane, Morocco
e
Arava Institute for Environmental Studies and the Dead Sea and Arava Science Center, Kibbutz Ketur D.N. Hevel Eilot, 88840, Israel
f
Department of Engineering Technology, University of Houston, 4800 Calhoun Rd, Houston, TX, 77004, USA
g
Electrical Engineering Department, College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
ARTICLE INFO
Keywords:
Big data
Cloud computing
Internet of things (IoT)
Smart agriculture (SA)
Wireless sensors networks (WSN)
ABSTRACT
It is widely acknowledged that traditional agricultural practices must effectively address the increasing global
demand for food while facing water scarcity and climate change challenges. The imperative for environmentally
sustainable agricultural approaches has never been more urgent. In response, IoT-based Smart Agriculture has
emerged as a promising solution. Smart Agriculture can signicantly bolster agricultural development by inte-
grating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems
utilizing big data acquisition and processing are pivotal in this advancement. This study introduces a cloud-based
smart irrigation system to connect numerous small-scale smart farms and centralize pertinent data. The system
optimizes irrigation water usage through comprehensive big data collection, storage, and analysis. Leveraging
the insights from this data can facilitate informed decision-making regarding water management, thereby
fostering conservation efforts, particularly in arid regions. Additionally, this research explores weather predic-
tion services to enhance irrigation control, particularly during intermittent rainy periods, within a real-world
testbed powered by solar energy. The testbed incorporates a sophisticated big data management system. It
showcases a Smart Farm prototype leveraging the Internet of Things, embedded systems, low-cost Wireless
Sensor Networks, NI CompactRIO controller, and Cloud Computing. Encouragingly, the results demonstrate
tangible improvements in water conservation. Furthermore, the deployment methodology outlined in this study
provides a clear roadmap that can be readily adapted for similar research endeavors.© 2023 Elsevier Inc. All
rights reserved.
1. Introduction
Agriculture has always been the backbone of human civilization
playing a crucial role in the global economy through food production
supporting economic stability and promoting sustainable methods [1].
The agricultural sector has evolved signicantly to meet the needs of a
growing world population tackle sustainability issues and adjust to
shifting climate conditions [2]. These continuous changes underscore
the resilience and enduring signicance of agriculture in today’s land-
scape [3]. Furthermore, agriculture plays a role in meeting the demands
of a growing population. Projections suggest that food production needs
to increase by 2050 [4]. Therefore, it is vital to adopt technologies and
enhance crop management practices to boost productivity. Additionally,
agriculture is leading the way in addressing climate change challenges.
However, the industry is vulnerable to risks posed by warming, such as
weather patterns, rising temperatures and varying levels of rainfall.
These shifts expose farmers to climate related impacts underscoring the
need, for strategies to manage these changes and sustain agricultural
productivity [5].
In the year 2022 agriculture played a role, in driving economic
* Corresponding author. REIPT Laboratory, Faculty of Sciences and Technology, BP 509, Boutalamine, Errachidia, Moulay Ismail University of Meknes, Morocco.
E-mail address: [email protected] (B. Et-taibi).
Contents lists available at ScienceDirect
Results in Engineering
journal homepage: www.sciencedirect.com/journal/results-in-engineering
https://doi.org/10.1016/j.rineng.2024.102283
Received 14 March 2024; Received in revised form 3 May 2024; Accepted 16 May 2024

Results in Engineering 22 (2024) 102283
2
expansion making up 4.3 % of the total global gross domestic product
(GDP) [6]. In Morocco agriculture holds importance by contributing
10.3 % to the country’s GDP during that same year. This achievement is
mainly credited to the Green Morocco Plan, which was launched by the
government back, in 2008 with the goal of enhancing agriculture as a
provider of jobs and fostering economic development over the next
decade to fteen years [7,8]. However, the agricultural industry is fac-
ing obstacles such as water supply, decreasing groundwater levels and
the broader effects of climate change. In the twenty years Morocco has
been utilizing a water volume ranging from 11 to 15 billion cubic meters
with around 75–87 % designated for irrigation purposes [9]. 40 % of this
irrigation water is sourced from reservoirs [10]. Recent statistics reveal
an extraction of groundwater amounting to 4226 million cubic meters in
comparison to a potential recharge capability of about 3404 million
cubic meters resulting in an annual shortfall of approximately 862
million cubic meters nationwide [11]. This disparity has led to depletion
and a signicant reduction in groundwater levels throughout all regions,
with declines varying between 20 and 65 m [12]. Table 1 presents the
Distribution of groundwater resources in different Moroccan basins.
According to the predictions about the climate, it is expected that
there will be droughts in the future leading to an increase in water
scarcity and agricultural sustainability challenges on a scale, including
in Morocco. This situation has raised concerns about striking a balance
between preserving resources and ensuring a food supply. Consequently,
the concept of sustainable agriculture is gaining popularity due to its
ability to conserve resources and maintain harmony. SA represents a
farming approach that leverages technology to meet the needs of society
while safeguarding the ability of future generations to meet their own
requirements [13]. This method focuses on minimizing agricultures
impacts on the environment while enabling farmers to make a living and
offer food options [14].
The integration of sensors and the IoT, in agriculture holds the po-
tential to revolutionize the way we cultivate food [15]. Through these
advancements farmers can. Analyze up to the minute information on
environmental conditions such as temperature, wind velocity, soil
moisture levels and even instances of res or smoke affecting their crops.
This enables farmers to pinpoint areas needing attention closely monitor
crop well-being and make decisions regarding planting schedules, irri-
gation needs, fertilization routines and harvest times using SA sensors.
These practices lead to wastage, heightened productivity and efciency
and a diminished ecological footprint. The data collected by sensors and
actuators is then stored in the Cloud Layer where cloud technologies
enable farmers to gather, store and analyze data swiftly in real-time
[16].
In this context, IoT technology combines the power of the internet,
with existing assets to enable remote supervision and control of devices
and systems [17]. This monitoring is made possible through the utili-
zation of communication technologies tailored to meet the requirements
of a modern farm’s infrastructure [18]. These technologies encompass
Bluetooth, ZigBee, Message Queuing Telemetry Transport (MQTT), Long
Range (LoRa), Wi Fi, General packet radio service (GPRS), 4G and 5G for
high-speed data transfer. This connectivity fosters interaction and data
exchange among devices leading to improved efciency and accuracy in
activities, through real time data gathering, analysis and implementa-
tion [19].
The combination of IoT and new technologies such as WSN and
Cloud Computing in agriculture brings benets that greatly improve the
effectiveness, sustainability, and protability of farming activities. One
key advantage is the boost in efciency. IoT allows for real time moni-
toring and control of processes leading to more accurate farming prac-
tices. This real time data collection helps determine the times for
planting, irrigation schedules and harvesting periods ultimately
enhancing operational efciency [20]. Furthermore, IoT systems aid in
resource management using sensors and data analysis reducing waste
and cutting costs associated with inputs like water, fertilizers, and pes-
ticides. For instance, soil moisture sensors ensure crops receive the
amount of water needed conserving water while boosting crop yields.
Moreover, these technologies enhance crop quality and yields by
monitoring conditions and adjusting practices accordingly. Farmers can
improve both the quality and quantity of their produce by detecting
diseases, predicting pest attacks, and keeping track of crop health in real
time. Innovations like drones, for surveillance automated tractors and
robotic harvesters enable precision agriculture that increases produc-
tivity while lowering labor expenses. Automation streamlines tasks
efciently and safely. Additionally, IoT combined with blockchain
technology can enhance transparency and traceability within the agri-
cultural supply chain [21]. To guarantee that customers get goods and
minimize wastage enhancing the supply chain’s efciency. Finally,
smart farming techniques aid environmental conservation by reducing
carbon emissions and promoting more sustainable land and water use.
The global market for agriculture has experienced growth due to
advanced technologies such as blockchain, articial intelligence (AI)
machine learning (ML) and IoT. As per a report by Precedence Research
the market was forecasted to be worth $18.12 billion initially then
surged to $21.89 billion in 2023 peaked at $91.91 billion in 2022 and
dipped to $8.24 billion in 2024 [22]. Despite facing these challenges, the
market showed signs of recovery reaching $24.24 billion in 2025 and
steadily climbing to $52.26 billion by 2026. Following a decline to
$21.32 billion in 2027 it rebounded to $24.35 billion in 2028. The
market is poised for growth with projections indicating it will reach
$43.37 billion by 2030 from an estimated value of $25.39 billion in
2029 [23]. Over the period from 2022 to 2030 a compound annual
growth rate (CAGR) of 10.2 % is expected to drive this progression.
Renewable energy is becoming more and more important, in farming
improving its sustainability and lessening its impact on the environment
[24]. As the agricultural industry aims for energy self-sufciency and
sustainability the use of energy sources like solar power, wind power
and biomass are gaining popularity [25]. These ecofriendly energy so-
lutions do not help decrease the carbon footprint of farming operations
but also lead to long term cost savings. Solar power specically is widely
Table 1
Distribution of groundwater resources in different Moroccan basins.
Basins Groundwater Resources Potential
(MCM)
Exploitable Groundwater Resource
(MCM)
Mobilized Potential
(MCM)
Over-Exploited Volume
(MCM)
Lukkos, Tangier, and Mediterranean
coasts
189 110 110 0
Moulouya, Figuig-Kert-Isly-Kiss 512 407 462 55
Sebbou 1301 1041 1143 102
Bouregreg and Chaouia 116 103 146 43
Oum Er-Rbia 406 335 457 122
Hauz 522 518 753 235
Souss-Massa and Tiznit- Ifni 371 369 646 277
Draa 371 296 323 27
Ziz-Gheriss-Guir and Maider 301 208 208 0
Saharian Basins 17 17 18 1
Total 4106 3404 4226 862
B. Et-taibi et al.

Results in Engineering 22 (2024) 102283
3
used in agriculture [26]. Solar panels can provide energy for farm needs
such as sensors, irrigation systems and even entire data centers. This
shift towards power makes farms more independent and reduces their
reliance on non-renewable energy sources. It’s especially advantageous
in areas with no grid electricity enabling smoother agricultural activ-
ities. Wind power is another asset for farms situated in windy regions.
Wind turbines can supplement a farms energy requirements lowering
electricity expenses and boosting sustainability [27]. This is particularly
benecial for farms with high energy demands like dairy operations that
rely heavily on refrigeration. Biomass energy sourced from leftovers,
like crop residues and animal waste offers two advantages. It transforms
waste materials into an energy source while addressing waste manage-
ment issues [28]. This method does not assist in handling waste but also
plays a role in promoting a circular economy within the farming sector.
Furthermore, improvements in battery storage technology are
enhancing the feasibility of these energy options. By storing surplus
energy generated during peak periods farms can guarantee an energy
source when energy production is low like on rainy days [29]. This
dependability is essential for the functioning of equipment and systems
that are vital to contemporary agricultural methods.
Despite the benets there are obstacles that hinder the adoption of
IoT technology in agriculture [30]. The initial costs associated with
implementing IoT, such as sensors, drones and data analysis tools can be
a barrier for farms. Integrating devices and systems can be complex and
may require specialized knowledge. Farmers might need training and
ongoing technical assistance to effectively utilize these technologies.
Dealing with the volumes of data generated by devices can be over-
whelming. Farms require data management and analytical capabilities
to extract insights from the data [31]. Successful implementation of IoT
relies on internet connectivity. However, many rural areas face chal-
lenges with connectivity limiting the use of solutions. Improved con-
nectivity raises concerns about data security and privacy collected
through devices. Farms must ensure their data is safeguarded against
cyber threats. Additionally, there is a lack of uniformity among devices
and platforms which can impede interoperability and integration of
systems. Power supply is essential for running devices posing challenges
in locations, without consistent power sources. This calls for integrating
energy sources that may entail set-up and maintenance requirements.
Our focus in this research is the creation of an irrigation system that
operates through the cloud connecting multiple small smart farms and
centralizing essential agricultural data. This system aims to maximize
water usage efciency by collecting, storing, and processing data
effectively promoting conservation of water and energy in agriculture.
We incorporate renewable energy sources and Utilize weather fore-
casting services to improve irrigation control addressing challenges
posed by unpredictable rainy conditions. This method makes use of
open-source technologies and off-the-shelf components to ensure cost
effectiveness and scalability making it a viable option for to small me-
dium sized farms in developing nations. Through presenting a real-
world deployment and demonstrating improvements in resource ef-
ciency in a real world setting our study provides practical insights and a
scalable framework that can be adapted for broader use, in the realm of
smart agriculture.
The rest of this paper is structured as follows; Initially we explore the
existing literature. Next, we provide an overview of the materials and
methods employed in this investigation. Subsequently, we showcase the
ndings derived from our study. In section 4 an in-depth discussion is
provided. Lastly, we wrap up our work and explore future research
directions.
2. Related work
The literature has extensively highlighted innovative strategies
aimed at improving water and energy resource management and agri-
cultural productivity using advanced technologies. First, Gupta et al.
[32] reviewed the current state of smart water technology for water
resource management. They also shed light on its challenges and future
scope. Next, Damas et al. [33] presented an automatic irrigation system
was developed in Spain, proving its potential to save water. The
experimental results of this research work showed that it could save
30–60 % of irrigated water. In addition, Kadar et al. [34] presented a
smart water management system prototype named AGRI2L. AGRI2L
puts forward a design to implement a low-cost smart water level and
leakage monitoring system that relies on real-time data. Such a system
makes the water resources management processes accurately and
effectively manageable. Adeoye et al. [35] presented an evapotranspi-
ration (ETo) model to accurately dene plants’ water requirements and
enhance managing limited water resources in arid regions using neural
Computing. Subsequently, Larios et al. [36] Suggested the best practices
to reduce water loss caused by traditional irrigation methods in Latin
America. This study’s most salient feature is integrating biochemistry
and the IoT in agriculture. This combination helps raise the competi-
tiveness of the agricultural economy and reduce water consumption.
The authors also provided a case study from the Guadalajara metro-
politan area, a mega-city needing innovative and eco-friendly solutions
to preserve its natural resources. Moreover, Macaroni et al. [37] cate-
gorized smart irrigation techniques into four main research areas: (1)
Crop evapotranspiration estimations based on remote sensing, (2) in-
formation and communications technologies (ICTs) for smart irrigation,
(3) models and controls for precision irrigation, and (4) the cost of
natural resources. Also, Giusti et al. have demonstrated a fuzzy control
system for irrigation [38]. The system uses inference and a predictive
soil moisture model to nd the best irrigation strategy for maintaining
soil moisture above the desired optimal level. The proposed system is
favorable compared to the existing agricultural models and the IRRINET
database. Testing it with random rainfall showed the model has greater
sensitivity and effectiveness, especially in saving water. The research
study was done by Zhu et al. [39] presented the hardware and software
designs of WSN-based agricultural environment monitoring systems.
The experimental results of this study showed the system’s high accu-
racy and running stability. Accordingly, these results demonstrated its
adaptability in remote, real-time farm monitoring. The research work
presented by Dasgupta et al. [40] takes advantage of IoT devices, WSN,
and AI technologies. It combines all these components to generate rec-
ommendations for farmers faster and more efciently. These recom-
mendations are based on several variables: temperature, rainfall, land
area, past crops that have grown history, and other resources. A deep
learning (DL) based model is also implemented for detecting unwanted
crops using a frame-capturing drone. The results showed that the Naïve
Bayes algorithm adopted has an accuracy of 89.29 %, and this rate is
much higher when compared to other algorithms such as regression and
Support Vector Machine (SVM). The Smart & Green system presented by
Campos et al. [41] offers smart irrigation services such as data
pre-processing, storage, monitoring, and irrigation scheduling based on
soil moisture predictions. Applying various outlier removal approaches
to the anticipated data reveals that this system can conserve irrigation
water by between 56.4 and 90 %. An efcient irrigation system based on
a database of the daily water needs of plants was developed by Munir
et al. [42]. This system species the exact amount of irrigation water a
plant needs based on the real-time soil moisture, humidity, and tem-
perature provided by IoT sensors. The implemented solution has a mo-
bile phone application for real-time monitoring and control of irrigation
systems. This eco-friendly solution supports smart energy consumption.
Results show that this system is efcient in water and energy con-
sumption and can be used for small and large elds. A straightforward
IoT sensors architecture that gathers environmental data and sends it to
the central server via a Wi-Fi network is proposed by Dagar et al. [43].
The server makes appropriate decisions in real-time, depending on the
IoT devices’ information that measures soil pH, soil moisture, temper-
ature, humidity, and water volume. An IoT-based solution for real-time
agriculture eld monitoring and irrigation control that contributes to
agriculture modernization in India and meets the agricultural sector’s
B. Et-taibi et al.

Results in Engineering 22 (2024) 102283
4
challenges, such as improving the agricultural elds’ productivity is
presented by Patil et al. [44]. This system connects both sensors and
irrigation control apparatus to the Cloud. This way, the system’s overall
architecture provides better analysis and problem-solving abilities.
WALLeSMART is a cloud-based solution that Roukh et al. [45] proposed
to develop a smart agricultural management system for the Wallonia
region in Belgium. This suggestion addresses the challenges of collect-
ing, processing, and visualizing massive amounts of agricultural data in
real time. A prototype was developed and evaluated with diverse cul-
tures, and the results showed outstanding efcacy. Kodandaramaiah
et al. [46] suggested a sophisticated IoT and cloud-computing-based
agriculture monitoring system. This system uses sensors to periodi-
cally collect temperature, humidity, soil moisture, and light inside a
greenhouse. Collected data is sent to the Cloud for real-time processing
and storage. This system’s ability to optimize the farmers’ resources and
its low cost make it available to everyone, including small farmers.
Froiz-Meguiez et al. [47] presented a smart irrigation system that
combines LoRa-based IoT sensors, fog computing gateways, and remote
cloud computing. The sensors collect real-time data and send it to the fog
or the Cloud to control the water supply. The results demonstrated the
system’s ability to reduce water consumption by 23 %. The role of the
computing model, deployment model, cloud service model, and the
benets and challenges of cloud computing in the agriculture sector are
discussed by Choudhary et al. [48]. An IoT-based soil moisture moni-
toring (IoT-SM) method using moisture sensors and an ET–based strat-
egy was compared in the study done by Kumar et al. [49]. This study
showed that the IoT system was durable and water-resistant, making it
suitable for outdoor agriculture. Climate-SA (CSA) is discussed by Bengü
Everest [50]. This study was conducted to introduce farmers ’adaptation
to climate change. Farmers’ perceptions of CSA were also taken into
consideration. The authors examined the literature to analyze the
adoption of CSA. They also presented a typology for CSA applications.
The results show that CSA classes have high application potential among
farmers. Helena Shilomboleni [51] highlights the three important
challenges of Africa’s CSA activities. This study provides an overview of
the contested discussions about CSA’s ability to comprehensively and
effectively respond to challenges that farmers encounter, especially
resource-constrained farmers in the south. Kamayab et al. [52] deeply
explore the inuence of farming, on climate change examining green-
house gas emissions, carbon storage and strategies to mitigate its effects.
They emphasize the signicance of nding solutions. Envision a future
driven by cutting edge technologies and collaboration among industries
to foster sustainability and resilience. This in-depth analysis underscores
the potential of agriculture in curbing emissions while ensuring food
security and biodiversity presenting a vision for the future. The design
and deployment of a weather station on Edamame farm are presented by
Tenzin et al. [53]. The results of a comparison between the meteoro-
logical data of this weather station and the data provided by the Davis
Vantage Pro2 installed within the same eld have shown the efciency
of the deployed weather station in measuring different weather pa-
rameters. Vangala et al. [54] delve into security challenges in modern-
ized farming, proposing a smart farming architecture and reviewing
current security protocols. Additionally, it assesses the latest industry
advancements in IoT-based tools. Cesco et al. [55] offer a framework for
designing farm information systems and apply it to a case study on crop
nitrogen fertilization, emphasizing the importance of understanding soil
and crop variability. The study also discusses challenges, especially for
areas with smaller farms, suggesting that digital twins can optimize
predictive analyses for both farmer benets and sustainable agriculture.
The research studies done by Morchid et al. [56] propose innovative
smart irrigation systems utilizing cloud computing, embedded systems,
and IoT to address agricultural challenges. These systems monitor
crucial environmental factors in real-time, achieving a notable 70 %
reduction in water consumption for soil irrigation. It also enables
farmers to access comprehensive farm data remotely, enhancing crop
production and sustainability, thus advancing food security. In addition,
they examine smart irrigation systems using IoT, Cloud Computing, DL,
and ML-based plant disease detection technologies for sustainable
agriculture. SaberiKamarposhti et al. [57]conduct an examination of the
greenhouse gas emissions in agriculture proposing the integration of AI
for monitoring and capturing carbon emissions. They also recommend
implementing strategies to decrease emissions and promote sustain-
ability. Emphasizing the importance of incentives and governmental
regulations, they present a thought-out strategy for the sector to
contribute to combating climate change. Sadri et al. [58] carefully tackle
obstacles in Cloud computing for IoT like delays by proposing fog
computing as a remedy for handling real time data in settings. Through a
review of literature. They examine studies on fog data management
between 2014 and 2019. They present a taxonomy based on context.
Delve into discoveries, benets, limitations, and upcoming hurdles, in
fog data management. Kamyab et al. [59] discuss the impact of AI and
Big Data Analytics on transforming water resource management in
overcoming issues related to acquiring real time data and making
informed decisions. They examine how AI and Big Data Analytics are
currently being used, including the application of machine learning and
deep learning methods for monitoring water quality, and predicting
water demand.
The review of existing literature identies signicant gaps in SA
solutions. First, the current commercial options heavily rely on devices
and software making them unaffordable, for many small and medium
sized farmers. Second, SA is mostly controlled using software. However,
there is a noticeable lack of open-source software that could promote
wider adoption of such innovative solutions. Third, there is an under-
utilization of weather forecast stations in SA which could signicantly
improve farming decisions with accurate predictions if utilized effec-
tively. Moreover, the incorporation of renewable energy sources like
solar power to feed in water pumps and IoT devices is not widely
implemented. This inclusion has the potential to lower carbon emissions
and reduce energy expenses. Finally, existing SA setups operate inde-
pendently without taking advantage of connecting smart farms within
regional or national networks. Establishing these connections will
generate big data that will be used by decision-makers for strategic
plans, enhance the optimization of resource sharing, and improve
agricultural practices at both regional and national levels.
To address these gaps identied in the study several new contribu-
tions are proposed. First, we opted for open-source software to handle
data collection, control, and actuation. Second, we created a cloud based
IoT platform to oversee data generated by various smart farms in real
time. Third, we used off-the-shelf Arduinos Nanos to construct low-cost
sensors and actuators thereby making SA technologies more accessible
to all agricultural entities including small and medium farmers. Addi-
tionally, to enhance water and energy management and support decision
making processes we have integrated weather predictions into our sys-
tem. In pursuit of sustainability and cost reduction our proposed SA
prototype fully relies on solar panels for energy needs. Moreover, we
suggest a setup where numerous smart farms are linked through a
centralized CompactRIO controller to optimize resource utilization and
elevate efciency. Furthermore, to optimize water and energy within
each smart farm across the region, we developed a smart irrigation
system that signicantly reduces water and energy use compared to
traditional methods. Finally, this study encompasses the implementa-
tion and assessment of a smart farm prototype with an emphasis on
application and data interpretation.
After delving into the frameworks and advancements in smart
farming. We are now moving forward to detail the materials and
Methods used in our study to ll the recognized gaps and add value to
the eld.
3. Materials and methods
In this section we outline the methods used to create our IoT based
irrigation system, which aims to improve farming techniques. We delve
B. Et-taibi et al.

Results in Engineering 22 (2024) 102283
5
into the systems structure highlighting elements, like WSN and the
National Instruments (NI) CompactRIO Controller. The design of our
Smart Farm model is explained with a focus on incorporating energy
sources and data collection capabilities. Additionally, we discuss setting
up the WSN integrating a data analytics platform connecting an online
weather station and creating algorithms for smart irrigation control.
This summary emphasizes the core methods driving our research in
advancing IoT enhanced agriculture.
3.1. Architecture of the proposed scheme
We developed a SA system that links several smart farms together
allowing them to share resources and cutting-edge features. As shown in
Fig. 1 our suggested smart irrigation system setup consists of ve parts;
WSN, individual smart farms, WAN, the NI CompactRIO Controller, and
a Cloud based IoT platform. These elements serve as the backbone of our
smart irrigation system guaranteeing connectivity and interaction, be-
tween the smart farms across a region.
At the heart of this structure lies the NI CompactRIO Controller,
purposefully designed for industrial control tasks. It acts as the hub that
oversees the irrigation operations throughout the network. The diagram
does not showcase how the system is built but also underscores how
different elements work together showing how information ows be-
tween farms and showcasing how the system can enhance farming ac-
tivities by making decisions based on data.
In the following parts we will thoroughly explore every element
detailing their functions and how they work together in the larger sys-
tem. Furthermore, by focusing on one of the smart farm we will conduct
a deep analysis of its key components. This approach will provide an
overview of the principles that apply to all smart farms connected to the
network.
To gain an insight into how farming integrates with renewable en-
ergy sources we suggest establishing a particular smart farm in our
network as a prototype. Our innovative smart farm prototype in-
corporates seven elements to create an interconnected farming system as
detailed in Fig. 2.
1. WSN: These sensors are strategically placed throughout the farm to
track factors, like weather conditions and soil quality. By collecting
data from locations, we gain valuable insights into microclimate
changes and can adjust farming practices accordingly.
2. WAN: These networks oversee devices such as water pumps
responding in time to needs by activating or deactivating them. This
proactive resource management boosts efciency and minimizes
unnecessary resource usage.
3. Cloud based IoT Platform: Serving as the hub of our network this
platform processes, analyzes and stores real-time data from different
sources including WSN, WAN, weather stations, farmers, and solar
panels.
4. NI CompactRIO Controller: This device conducts real-time analysis
of inputs like temperature and soil moisture levels. By utilizing this
data, it optimizes irrigation schedules to meet the requirements of
different crops across various farms, within our network.
5. Solar Energy System: The solar energy system integrates solar panels
to provide energy, reducing the farms’ dependence on traditional
power sources and supporting environmental sustainability.
6. Energy Control Unit: This unit optimizes the distribution and usage
of energy produced by the farm ensuring effective power manage-
ment across agricultural activities.
7. Storage: This unit, in the form of batteries, stores excess energy
generated during peak production periods. This stored power is then
utilized later to maintain an energy supply and strengthen the farm’s
power system resilience. In this study, lithium-ion batteries with a
total energy capacity of 3.3 kWh are used to store energy.
In this study, we developed a WSN using the Arduino Nano micro-
controller to enhance crop cultivation by managing water supply and
monitoring the environment. This network includes sensors, like the
Temperature and Humidity Sensor (DHT 11), Fire Sensor, PIR Motion
Sensor and Soil Moisture Sensor. Our choice of sensors and the Arduino
Nano was inuenced by our aim to offer a cost-effective solution, for
small and medium scale farmers who might nd commercial options too
pricey.
In order to respond to the different needs of agricultural environ-
ments, especially considering the varying communication distances
required. We opted for two types of WSN; GPRS based, and ZigBee based
WSNs. The use of GPRS technology supported by a SIM900 GPRS
module allows for long distance communication making it well suited
for large agricultural areas. On the hand, ZigBee communication tech-
nology offers less energy consumption and short distance communica-
tion capabilities making it perfect for specic areas. This strategic choice
ensures that our network can adapt to both scale and localized agri-
cultural settings, improving energy efciency, and enabling customized
Fig. 1. Testbed general architecture.
B. Et-taibi et al.