Farming and agricultural production account for a substantial part of the global economic system, and most people rely on them for their living. In this perspective, real-time agricultural field monitoring and smart irrigation using modern technologies are now important for effective farming in green homes, smart cities, and rural areas. Water is an essential resource to be conserved using the newest technology. The Internet of Things (IoT) and Industry 4.0 enable smart farming, including using Quadrotor unmanned aerial vehicles (Q-UAV) with computer vision. The IoT-based smart irrigation management systems with real-time sensors and Q-UAVs have contributed to the optimum use of water resources in precision farming. The research presented an intelligent irrigation and field surveillance system using atmospheric and soil data such as temperature, humidity, salinity, wind speed, as well as photographs of the field using UAVs. The parameters mentioned above are available on the smartphone of the farmers using IoT and are hosted without any delay in the Firebase console. In addition to this, a user can control the water pump on various fields via Firebase Cloud Message platform. The intelligence and smartness of the proposed system are implemented with a powerful and low-cost platform Raspberry Pi 4B system on chip computer with Industry 4.0 standard dedicated for IoT, real-time embedded protocol interfacing, and computer vision applications.

1. Received: 1 January 2022 Accepted: 4 March 2022 Published online: 26 May 2022
DOI: 10.1002/agj2.21061
S P E C I A L S E C T I O N : A DVA N C E S I N R E M O T E S E N S I N G F O R
P R E C I S I O N AG R I C U LT U R E
Real-time agricultural field monitoring and smart irrigation
architecture using the internet of things and quadrotor unmanned
aerial vehicles
Rajalakshmi Selvaraj1
Venu Madhav Kuthadi1
S. Baskar2
1Dep. of CS & IS, Botswana International
Univ. of Science and Technology (BIUST),
Palapye, Botswana
2Dep. of Electronics and Communication
Engineering, Karpagam Academy of Higher
Education, Coimbatore, India
Correspondence
Rajalakshmi Selvaraj, Dep. of CS & IS,
Botswana International Univ. of Science and
Technology (BIUST), Palapye, Botswana.
Email: rajalakshmiselvaraj270@gmail.com
Assigned to Associate Editor Priyan
Malarvizhi Kumar.
Funding information
Department of Science and Technology
(DST), New Delhi, India, for the funding to
carry out the Research work -
DST/TDT/AGRO-20/2019 & 22-01-2020
Abstract
Farming and agricultural production account for a substantial part of the global
economic system, and most people rely on them for their living. In this perspective,
real-time agricultural field monitoring and smart irrigation using modern technolo-
gies are now important for effective farming in green homes, smart cities, and rural
areas. Water is an essential resource to be conserved using the newest technology.
The Internet of Things (IoT) and Industry 4.0 enable smart farming, including
using Quadrotor unmanned aerial vehicles (Q-UAV) with computer vision. The
IoT-based smart irrigation management systems with real-time sensors and Q-UAVs
have contributed to the optimum use of water resources in precision farming. The
research presented an intelligent irrigation and field surveillance system using
atmospheric and soil data such as temperature, humidity, salinity, wind speed, as
well as photographs of the field using UAVs. The parameters mentioned above are
available on the smartphone of the farmers using IoT and are hosted without any
delay in the Firebase console. In addition to this, a user can control the water pump on
various fields via Firebase Cloud Message platform. The intelligence and smartness
of the proposed system are implemented with a powerful and low-cost platform
Raspberry Pi 4B system on chip computer with Industry 4.0 standard dedicated for
IoT, real-time embedded protocol interfacing, and computer vision applications.
1 INTRODUCTION
Unmanned aerial vehicle (UAV) technology and smart sen-
sors have the potential to assist farmers across the globe
in various ways, including monitoring crops, paddy fields,
Abbreviations: ADC, analog to digital converter; GPIO, general-purpose
input–output; HTTP, hypertext transfer protocol; I2C, inter-integrated
circuit; IDLE, Integrated Development Learning Environment; IoT, Internet
of Things; Q-UAV, Quadrotor unmanned aerial vehicle; RPi, Raspberry Pi;
SCL, serial clock line; SDA, serial data line; SoC, system on chip; SPI,
serial peripheral interface; UAV, unmanned aerial vehicle; XMPP,
extensible messaging and presence protocol.
© 2022 The Authors. Agronomy Journal © 2022 American Society of Agronomy.
improving land tenure, and more (A. López et al., 2019).
However, to achieve its full potential, regulatory frameworks
are required. This paper presented a real-time implementa-
tion of a smart agricultural irrigation architecture using the
Internet of Things (IoT) and vision-assisted field monitor-
ing via a cloud server, namely the Firebase platform. This
architecture offers several significant benefits compared with
conventional remote monitoring methods such as satellites
and human-crewed aircraft. Using real-time sensor data, the
framers can earn more money and reduce production costs
by making intelligent decisions based on the current state of
the paddy field. Drip irrigation facilities can be made more
Agronomy Journal. 2023;115:1–20. wileyonlinelibrary.com/journal/agj2 1
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2. 2 SELVARAJ ET AL.
efficient by sensor data, such as temperature, humidity, and
soil moisture.
In addition to this, the framework based on the Rasp-
berry Pi (RPi) system on chip (SoC) computer can capture
high-definition images of crops, animals, and other objects in
agricultural fields through direct interaction between farmers
and the UAV vehicle via a remote controller or the Firebase
cloud server. Perhaps most significantly, this framework with
Quadrotor unmanned aerial vehicle (Q-UAV) is cheap, cost-
ing less than US$300. Due to the use of customized sensors
and open-source programming platforms such as Open Com-
puter Vision and Python Integrated Development Learning
Environment (IDLE), the overall cost of this system has been
lowered to a minimum. The Q-UAV technology is intended to
benefit farmers in underdeveloped nations using current tech-
nological advancements such as embedded systems and com-
puter vision architecture to assist them in their farming oper-
ations. There is no need for a human pilot or travelers to be
UAVs for drones, and it is common for drones to be remotely
piloted by a human, even though this is not always the case.
Agriculture will benefit significantly from UAVs. The
UAV data will become increasingly automatic for identifying
different crop varieties, categorizing weeds, and assessing
crop damage caused by pests, among other things (Albu-
querque et al., 2020; Shi et al., 2018). The Q-UAVs with
more intelligence may be used for precision agricultural
spraying, allowing farmers to use fewer pesticides and reduce
human interaction with potentially hazardous compounds
while increasing crop yields. Getting sick is a component of
living that we have no control over. This means that sickness
absence is a part of working life, and it is unavoidable. The
strategies to be checked are as follows:
1. A physical or mental ailment that is real.
2. A way of life that is detrimental to one’s health.
3. Caring for loved ones is a necessity.
4. Problems with one’s own emotions.
5. Understanding sick leave policies is a problem.
Agriculture continues to account for a significant portion
of global commercial growth, and financial investments in the
agricultural sector have grown significantly in recent years as
a result. Pets and harmful insects reduce the potential produc-
tion of crops, reducing their overall yield. The use of UAVs for
pesticide and fertilizer spraying has significantly decreased
the incidence of health problems and the number of employ-
ees (Rahman et al., 2021). The UAVs and intelligent moni-
toring systems with powerful computer platforms are essen-
tial components of the agricultural revolution (Kataev et al.,
2019; Rodriguez-Galvis et al., 2020). The framers may earn
more profit and reduce the production cost using the sensor
data gathered in real-time and make intelligent decisions on
the current paddy state. Sensor data like temperature, humid-
Core Ideas
∙ Internet of Things and Industry 4.0 enable smart
farming, which includes the use of Quadrotor
unmanned aerial vehicles.
∙ A user can control the water pump on various field
via Firebase Cloud Message platform.
∙ The intelligence and smartness of the proposed
system is implemented with a powerful and low
cost Raspberry Pi 4B.
ity, and soil moisture assist farmers in enabling the drip irriga-
tion facility (Mahbub et al., 2020; Ogidan et al., 2019). Inno-
vative farming systems based on embedded systems, com-
puter vision, and the IoT are gaining popularity and interest to
increase food production (Cao et al., 2019; Serdaroglu et al.,
2020). During the automate to detect different crop varieties,
classify weeds, and analyze crop damage caused by pests, the
cropping calendar encompasses everything from land prepa-
ration to planting to harvesting. Crop spectral reflected light
is referred to as the crop’s temporal profile at each of these
stages of growth.
The research work presented using IoT and computer vision
has two main parts. Firstly, Q-UAV development provides
remote vision-based monitoring with RPi 4B hardware and
a Firebase cloud platform. The second component plays an
integral part in automation, including environmental and soil
factors that directly influence crop production and the sustain-
ability of the agricultural community. There is no need for a
human pilot or travelers to be UAVs for drones. It is common
for drones to be remotely piloted by a human, even though
this is not always the case. The proposed framework with dig-
ital and analog sensors measures the real-time physiological
parameters. The irrigation system and other automatic devices
are controlled by the general-purpose input–output (GPIO)
lines of the RPi 4B model. The following sections have been
included in this research article: some relevant works (section
2), system architectures for smart farming, including hardware
and sensor interfacing, protocols and cloud applications (sec-
tion 3), application of Q-UAV on agriculture field monitoring
(section 4) and real-time experimental set-up (section 5) fol-
lowed by the conclusion.
2 RELATED WORK
Tiglao et al. (2020) have suggested a wireless sensor and actu-
ator network-based Agrinex system (WSAN). The framers
may earn more profit; furthermore, the sensor data gath-
ered in real-time made intelligent decisions on the current
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3. SELVARAJ ET AL. 3
paddy state. Sensor data like temperature, humidity, soil mois-
ture, etc., could facilitate farmers to enable the drip irri-
gation facility. Their mesh network has been developed to
reorganize the sensors depending on the weather conditions.
Canales-Ide et al. (2019) have created a Water Use Land-
scape Species Classification (WUCOLS); it estimates the
plant water requirements based on plant species composites.
Humans manage irrigation scheduling and operation by cal-
culating crop coefficients and irrigation frequency based on
climatic variables. Zhu et al. (2021) studied optimum routing,
aborting, and striking methods for UAVs. They considered
parameters such as fuel load, window time, and other vari-
ables, and their model reduced the likelihood of UAVs being
destroyed. Architectural strength is used in interconnected and
intelligent smart agriculture to address privacy and security
issues. Their multi-faceted design resolved cyber attacks in
the food system, internet security issues, and most substantial
difficulties and covers in these smart agriculture issues.
Bu and Wang (2019) have developed an IoT and machine
learning architecture in an intelligent agricultural environ-
ment; enhances food production via the use of today’s tech-
nology such as artificial intelligence (AI) and cloud comput-
ing. In particular, profound enhancements in the cloud layer
lead to rapid choices such as the water must be rinsed to
improve the culture-growth environment. Reghukumar and
Vijayakumar (2019) suggested IoT-based real-time agrifarm
monitoring and decision-making minimize farmers’ effort by
busing intelligent sensors and actuators. Their concept over-
comes the limitations of state-of-the-art techniques with intel-
ligent farming via IoT and helps farmers assess their portable
gadget data. It removes the essential requirement for contin-
uous human surveillance on their paddy fields. Gupta et al.
(2020) developed a robust architecture in intelligent and inter-
connected smart farming to resolve data privacy and safety
concerns. Their multi-faceted design addressed cyber assaults
in the food supply chain, cybersecurity problems, and the most
significant difficulties and research concerns in these intelli-
gent agriculture issues.
In precision agriculture, C. López et al. (2021) addresses
the issue of picture fusion. They created a multi-layer regres-
sion model for UAV pictures, enabling aerial images with sub-
stantial differences to be connected. The effectiveness of the
Enhanced Correlation Coefficient technique is an adequate
way to record diverse views. Allreda et al. (2020) presented
a new way of finding drainage pipes using thermal infrared
(IR) imaging. Three techniques have been highlighted: visi-
ble color, multispectral, and thermal infrared imagery using
UAVs. The models mentioned above offer considerable map-
ping potential for agricultural drainage pipes. Li and Fang
(2021) have developed and classified the formation deci-
sion function in UAV virtual point formation control mode
at an anticipated angle based on the pigeon swarm behav-
ior tracking model. Their simulation findings prevent colli-
sions between UAVs and different barriers, effectively con-
trol UAVs, and extend the UAV swarm application. An IoT
and machine learning architecture in an intelligent agricul-
tural environment improves food production. When the cloud
layer improves significantly, rapid decisions are made, such as
rinsing the water to improve the culture-growth environment.
Liao et al. (2021) have created an intelligent irrigation sys-
tem using real-time soil parameter monitoring. They stressed
the significance of irrigation planning and provided insights
into designing an effective and automated irrigation sys-
tem. Nawandar et al. (2019) highlighted the need for natu-
ral resource conservation and justified the need for intelli-
gent, automated systems. Their standardized approach based
on IoT and industry 4.0 enhances the demand for intelli-
gent sensors in agriculture, particularly irrigation and pest
management. The neural network gives the device the nec-
essary intelligence, considering current sensor information
and masking the irrigation schedule for adequate watering.
Podder et al. (2021) presented a rural community IoT-based
Smart AgroTech system considering crucial characteristics
of soil and environment for agricultural fields. The choice
of the AgroTech system on irrigation relies on the farming
circumstances. The vendor may see and analyze the sen-
sor data through a remote monitoring system. The system
ensures that the agricultural operations in future cities ben-
efit from a viable Smart AgroTech system that other tradi-
tional techniques. The Improved Multiple Regression tech-
nique’s effectiveness is adequate for recording various points
of view. Thermal IR imaging provides a new method for
locating drainage pipes. It has been discussed how UAVs
can capture appearance, hyperspectral, and near-infrared
imagery.
Based on a literature study analysis, it is evident that there
are many intelligent irrigation methods and UAVs to main-
tain friendly and cost-effective farming. Current systems have
some limits, such as flexibility with farmers, optimum IoT and
sensor platforms, multiple node systems, conserving water
and electricity, etc. This article highlights the improvement
of intelligent irrigation and real-time field monitoring in rural
and urban agriculture. The primary goals of the system sug-
gested are as follows:
1. Real-time deployment of IoT and computer vision for auto-
mated irrigation control systems.
2. Deploy an intelligent and low-cost irrigation system with
a soil moisture sensor, temperature, rain indicator, wind
speed, and humidity sensor.
3. Develop an intelligent agriculture-filed monitoring system
that collects real-time filed images using Q-UAVs, thus
lowering labor costs.
4. Analyzes the importance of bringing the suggested smart
system into a rural and innovative agricultural environ-
ment and set the user interface to a Firebase cloud.
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4. 4 SELVARAJ ET AL.
3 METHODS AND TECHNOLOGIES
USED IN INTERNET OF THINGS BASED
SMART IRRIGATION SYSTEM FOR
AGRICULTURAL FILED
This section presented an intelligent agricultural field surveil-
lance system using smart sensors and a powerful GPU com-
puting platform. The suggested framework is mainly devel-
oped using RPi 4B; it is a SoC platform with Broadcom
BCM2711, Quad-core Cortex-A72 (ARM Version 8) 64-bit
SoC @ 1.5 GHz 8GB LPDDR4- SDRAM. Linux or Windows
operating system power this low-cost platform with a credit
card-sized platform. This computing platform includes data
processing to improve decision-making and supports it.
Architectural strength is used in interconnected and intelli-
gent smart agriculture to address privacy and security issues.
Their multi-faceted design resolved cyber attacks in the food
system, internet security issues, and most substantial difficul-
ties and covers in these smart agriculture issues. The RPi is a
low-power SoC computer with customizable GPIO pinouts, a
robust CPU that can run Linux, and supports NodeJS, making
it easy to create complex devices. In general, it is the route to
Industry 4.0, which supports IoT, 5G connection, and artifi-
cial intelligence automated industrial systems. The next part
discusses the complete architecture of this suggested system,
including the real-time sensor implementation aspects.
Various analogue and digital sensors are used to mea-
sure atmospheric and soil parameters, which are described
in Figure 1. The proposed system is implemented with five
later architectures; the bottom layer collects real-time sensor
data. In this physical layer, a sensor like DS18B20, DHT11,
customized soil moisture, wind speed, and rain sensors are
connected to the RPi SoC unit using inter-integrated circuit
(I2C) protocol and MCP 3008 analog to digital converter
(ADC). An I2C protocol is used to communicate with low-
speed peripherals. Depending on your board’s model and revi-
sion, there may be one or two I2C buses on it. The serial data
line (SDA) and a serial clock line (SCL) are the two input
lines on each bus connected to an I2C center. Analog sen-
sors are interfaced to RPi via the ADC eight-channel module;
this module has provided the digital conversion process and
communicated through the I2C protocol bus to the RPi sys-
tem. The Firebase platform configures a real-time database;
it is a dedicated cloud architecture supporting images, audio,
and video streaming applications. Conservation is the preser-
vation and safeguards of these resources to preserve these
resources for the future. Preserving nature from human inter-
ference is the goal of conservation; on the other hand, conser-
vation aims to sustain human activities like predation, able to
log, and mining.
This research aims to develop a low-cost and accessible
smart irrigation system for rural and farmer’s communities
by using technical progress in sensors and embedded sys-
tem sectors. The suggested system thus introduces a Q- UAV
framework for real-time monitoring and sensors for the cloud-
based irrigation management mechanism. The sensors used
are described in the following subsections. The number of
power strips should be restricted. An overheating problem can
occur if too many power strips use a single outlet. Regularly
hire a professional electrician to inspect your wiring for signs
of wear and tear. Perform an appliance inspection.
The IoT architecture used in this research is based on
different sensors, layers, and functionalities, as shown in
Figure 2. The IoT is a linked network of physical devices,
sensors, and software-embedded protocols. The developed
architecture has been integrated into small business and
agriculture areas, providing end-to-end solutions by combin-
ing IoT and computer vision characteristics. This research
transforms IoT systems seamlessly, providing complete
device management and real-time monitoring. Users may
access sensor data in a standard cloud database without inter-
ruption when using a mobile internet connection. By using
extensible messaging and presence protocol (XMPP) and
hypertext transfer protocol (HTTP), the proposed system’s
internet layer can communicate the water pump status to the
remote sensor module (HTTP). Figure 2 shows each layer
representing the corresponding role for smart irrigation and
UAV implementation in the agricultural field. The suggested
system is multi-configured; it works in wholly automated
mode. Sensor values are shown in the cloud database in
the standard architecture and may access the user without
interruption through their mobile internet. The internet layer
of the proposed system has included a unique capability to
communicate the water pump status with a remote sensor
module using XMPP and HTTP. This function allows the user
to turn the motor ON/OFF through the Firebase interface.
This research bids real-time monitoring and control of motor
pumps and other agricultural equipment without human
presence. It will enhance the efficacy and safety of human
beings and prevent overheating and other current surge issues
for electrical and mechanical equipment. The following
section provides a comprehensive examination of layered
architecture. Connected devices, sensors, and software-based
protocols form the IoT. End-to-end solutions that combine IoT
and computer vision characteristics have been implemented
in small businesses as well as in agriculture.
The research and analysis have been conducted in three
main agriculture fields. The performance of developed hard-
ware was tested on 0.3 ha of the vegetable greenhouse, 1 ha
of mixed plants such as tapioca (Manihot esculenta Crantz)
and banana (Musa X paradisiaca L.), and 1 ha of coconut
(Cocos nucifera L.) plantation. For these three fields, aver-
age temperatures vary, and average sunshine hours of 9–
10 h. The research was split into a three-tier structure: from
March to June, July to October, and November to February in
1 yr. Agricultural production is based on many variables; key
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5. SELVARAJ ET AL. 5
F I G U R E 1 The architecture of smart irrigation systems using Internet of Things. ADC, analog to digital converter; GPIO, general-purpose
input–output; HTTP, hypertext transfer protocol; SCL, serial clock line; SDA, serial data line; XMPP, extensible messaging and presence protocol
characteristics include carbon dioxide, temperature, solar irra-
diation, precipitation, soil moisture, wind speed, direction,
etc. The detailed analysis of sensors used in this research is
available in the subsequent sessions. The proposed system’s
internet layer includes a unique feature that allows the remote
sensor module to communicate with the water pump’s status
via extensible messaging.
3.1 Embedded protocol layer
The primary communication need for the interaction of
hardware and memory processing units is the internal
communication or on-board communication protocol. Three
major protocols are used in embedded systems; the I2C,
the serial peripheral interface (SPI), and the universal
asynchronous receiver transmitter. The I2C is the popular
communication standard for the RPi digital sensor interface.
The basic I2C setup using real-time sensors and RPi is shown
in the diagram below. The use of unmanned aircraft will
greatly aid farming. There will be an increase in drone data
to identify crop varieties, classify weeds, and assess pest-
damaged crops in the future. Precision agricultural spraying
can be improved with more intelligence, allowing farmers to
use fewer pesticides and reduce human exposure to potentially
hazardous compounds while increasing crop yields.
Figure 3, in terms of communication, uses a synchronous,
two-line, half-duplex configuration. DS18B20 and DHT11
sensors are used as slaves by the RPi in this experiment.
Inability to talk with and control synchronous master and slave
devices, they use the serial communication line and the serial
countdown. The master–slave configuration of sensors and
RPi is shown in Figure 3. Each I2C bus device has a seven-bit
hardware address with a transmission rate of 100 kbps. The
I2C can handle up to 127 devices using two lines named SCL
and SDA with a seven-bit address. The first byte contains a
seven-bit address and a read/write bit followed by the actual
data during transmission. It is a one-wire protocol accessible
through the GPIO pins of RPi. It operates in a synchronous
two-line half-duplex mode of communication. Raspberry Pi
serves as the master device in this research, with sensors such
as the DS18B20 and DHT11 serving as slaves. They can
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6. 6 SELVARAJ ET AL.
F I G U R E 2 The five-layer architecture of real-time agricultural field monitoring and smart irrigation architecture using Internet of Things and
Quadrotor unmanned aerial vehicles. FCM, Firebase Cloud Message; HTTP, hypertext transfer protocol; SCLH, serial clock line high; SDAH, serial
data line high; VDD, voltage drain drain; XMPP, extensible messaging and presence protocol
F I G U R E 3 The inter-integrated circuit communication interface
between sensors and Raspberry Pi. SCL, serial clock line; SDA, serial
data line
interact and control synchronous master and slave devices via
two lines: SDA and SCL.
The parameter that can handle up to 127 devices using 196
two lines called SCL and SDA and a seven-bit address, with
IDs are 3 bytes long and contain three fields: the manufac-
ture ID, the device ID, and the die revision number, each
with a length of 12/9/3 bits. The device ID is a 7-bit physi-
cal address of the sensor; it is used to distinguish the sensor
data received through the I2C line. The transfer diagram indi-
cates in Figure 4 that each byte on the SDA line is 8-bit length
followed by an acknowledgment signal. The number of bytes
sent to each transmission is not limited, and data transmission
continues until the slave is ready for another byte of clock line
SCL and releases. The device ID of sensors is 3-bytes long
with three fields; manufacture ID, device ID, and die revision
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7. SELVARAJ ET AL. 7
F I G U R E 4 Data transfer diagram in the inter-integrated circuit protocol (Ref. I2C datasheet no. UM10204). ACK, acknowledgment; MSB,
most-significant bit; SCL, serial clock line; SDA, serial data line
number with 12/9/3 bit long, respectively. A device ID is a
7-bit physical address of the sensor; it is used to differentiate
the sensor data received through the I2C line.
3.2 Physical layer
The physical layer or perception layer hosting intelligent IoT
includes various devices such as sensors, actuators, machines,
and motors. The sensor devices gather real-time data; the actu-
ators are used to automate electrical electronic equipment.
Machines and appliances are part of this layer linked to the
other two utilities. The working and role of the numerous sen-
sors used in this proposal will be demonstrated in-depth in the
next sections.
3.2.1 DS18B20 Temperature sensor
Plants require four things; light, water, soil, and air. To grow
healthy plants, the essential component is water impact. The
DS 18B20 is a unique digital temperature sensor with 64-bit
serial data output and one-wire communication compatibility.
It has an internal configurable ADC with 9 and 12-bit resolu-
tion. The system can control multiple devices distributed over
a large area because the RPi computer integrates with serial
communication protocols like I2C and SPI. Here the temper-
ature sensor is configured for 12-bit resolution and connected
to the I2C pin of RPi; the 4th general-purpose input–output
pin (GPIO-4) is configured to connect the digital tempera-
ture sensor. This unit measures the atmosphere temperature
from −55 to +125˚C with an accuracy of ±0.5˚C for −10˚C
to +85˚C. The interfacing circuit of DS18B20 with RPi is
shown below.
In Figure 5, the complete interfacing circuit of RPi and
DS18B20 temperature sensor. To sustain the higher leakage
current, power the unit from GPIO pins of RPi. A water-
proof model of the DS18B20 sensor is selected, and it is
best appropriate for this hydro project. Some of the proto-
F I G U R E 5 Interfacing circuit of Raspberry Pi with DS18B20
temperature sensor. DQ, digital data output; GND, digital ground;
GPIO, general-purpose input–output; SCL, serial clock line; SDA,
serial data line; VCC; voltage collector collector; VDD, voltage drain
drain; VSS, voltage source source
cols used in embedded systems are conventional protocols for
serial peripheral interfaces, including SPI, I2C, universal syn-
chronous/asynchronous receiver/transmitter, and control area
network. The RPi CPU works as the master and other sensors
as slaves. A pull-up resistor with 470 Ω is used between digital
output and VDD pin; digital output pins are a tri-state or open-
drain port used to help the master unit identify the 1-Wire bus
temperature conversions. Each DS18B20 has a unique 64-bit
code stored in ROM. The least significant 8 bits of the ROM
code contain the DS18B20’s 1-Wire hardware address stating
28 h; this address is used to provide an error-free communi-
cation between master and slave as given below.
Temp_device_folder = glob.glob
(′
∕sys∕bus∕w1∕devices∕′
+′
28∗′
)
[0] (1.1)
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8. 8 SELVARAJ ET AL.
device_file = device_folder +′
∕w1_slave′
(1.2)
Equations 1.1 and 1.2 are used to detect the data from
the I2C sensor connected to the RPi. The I2C protocol layer
continuously checks for the data packet from any sensors
with starting address of 28 h. The following 48 bits have a
unique serial number followed by an 8-bit error correction
code. Equation 1.1 finds the slave devices with an address
28 h and reads the data packet using Equation 1.2. The data
from the I2C sensor connected to the RPi is detected using
these equations. The I2C protocol layer executes continuous
data packet checking for sensors with starting addresses of
28 h, and higher An 8-bit error-correcting code follows the
unique serial number in the remaining 48 bits. The resolution
of DS18B20 is improved using the Equation 2; the values for
the register named Count Per˚C (Rcount) and Count Remain
(Crem) are used in this equation.
Temp = Temp𝑎𝑑𝑐 − 0.25 +
(
𝑅count − 𝐶count
/
𝑅count
)
(2)
In Equation 2, Tempadc is the digital equivalent of temper-
ature read from ADC register, Rcount and Crem are the count
values of the ADC registers accessible through the I2C proto-
col. The data received from this unit is hosted to the firebase
database in real-time. The end-user or framer may see and
analyze data from their portable gadget such as smartphones
and personal digital assistants. The temperature and humidity
control systems are required to rate plant development, pro-
duction capacity, and product quality. Various experimental
experiments were conducted over time. A common applica-
tion for I2C is reading information from sensors and control-
ling certain components via a master–slave bus protocol. The
RPi provides the master, and all of the slaves are attached to
it via a single bus.
The information in this collection is used to accomplish the
effects depicted in the following diagram.
Figure 6 displays the temperature readings acquired
through the DS18B20 sensor from March to June 2021. The
created system constantly monitors the above characteris-
tics and transmits them to the firebase cloud without delay.
These parameters are beneficial for the farmers, particularly
for greenhouse platforms to schedule drip irrigation.
3.2.2 DHT11 Humidity sensor
Temperature or humidity imbalances may have a range of
harmful effects on plants and possibly cause harvests to be
squandered. Moisture is the actual water vapor concentration
percentage at a given temperature and pressure. It directly
affects plant water relations and indirectly affects leaf devel-
opment, disease likelihood, and economic yield. DHT11 has
a surface-mounted negative temperature coefficient thermis-
tor and resistive moisture sensor. It transforms thermistor and
humidity sensor resistance data into digital temperature and
relative humidity measurements. The relative humidity of a
sensor is defined by the equation, which is as follows:
𝑅Hum = 𝑅Ref_Hum
[(
𝐶Cap − 𝐶_Cap_bulk
)
∕Sensitivity
]
+ Temp_Dep
(3)
According to the humidity sensor used, there is a different
way to calculate the temperature dependence (Temp_Dep) of
the relative humidity measurement (RHum). The capacitance
of the sensor is represented by CCap and bulk capacity using
C_Cap_bulk. In cultivating healthy plants, water impact is essen-
tial. One-wire communication and 64-bit serial data output
make the DS 18B20 a truly unique digital temperature sen-
sor from Equation 3, for a sensor’s relative humidity, is cal-
culated. The temperature-dependent measurement of relative
humidity can then be obtained. The sensitivity of the sensor
is calibrated using the equation:
Sensitivity =
(
Cap_𝑅95Hum
− Cap_𝑅10Hum
)
∕85% (4)
Equation 4 indicated that sensitivity is the measure of the
difference of capacitance of the sensor at 95% (R95Hum) and
10% of relative humidity R10Hum.
Figure 7 shows the real-time sensor interfacing with RPi,
connecting digital sensor output to GPIO 3 using one wire
communication technique. The sampling rate of DHT11 is
1 Hz or one reading per second, and the working voltage is
3–5 volts of 2.5 mA. It can measure relative humidity from
20 to 90% and temperature from 0 to 50 ˚C. Due to its limited
temperature range, DHT11 is dedicated to detecting humidity,
and DS18B20 is available in sealed packaging with an exten-
sive temperature range. The DHT11 humidity value is sent to
the firebase console and is seen in the farmer’s mobile unit
using the IoT infrastructure.
The real-time humidity readings from the DHT11 sensor
of field 1 are displayed in Figure 8. Many variables, includ-
ing humidity, produce difficulties like foliar and root diseases,
grade loss, etc. So more pesticides are needed to control dis-
ease, and plants have weak, stretched growth, making them
unattractive. Low humidity stunts plant growth, causing crops
to mature slowly. Low humidity affects quality, boosts pro-
duction costs, and reduces profits. The use of unmanned air-
craft will greatly aid farming. There will be an increase in the
use of drone data to identify crop varieties, classify weeds,
and assess pest-damaged crops in the future. Precision agri-
cultural spraying can be improved with more intelligence,
allowing farmers to use fewer pesticides and reduce human
exposure to potentially hazardous compounds while increas-
ing crop yields.
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9. SELVARAJ ET AL. 9
F I G U R E 6 Real-time temperature readings from field 1 from March to June 2021
F I G U R E 7 Interfacing circuit of Raspberry Pi with
DHT11humidity sensor. DQ, digital data output; GND, digital ground;
GPIO, general-purpose input–output; SCL, serial clock line; SDA,
serial data line; VCC; voltage collector collector; VDD, voltage drain
drain; VSS, voltage source source
3.2.3 Soil moisture and salinity sensor
Soil moisture management is an essential farming component
for improving productivity and farmers’ commercial position.
Farmer’s ultimate goal is to improve water storage and mois-
ture efficiency. Unfortunately, rural society still has a diffi-
cult job and needs additional technical assistance with intel-
F I G U R E 8 Real-time humidity readings from DHT11 from field
1 from March to June 2021
ligent sensors and automated irrigation management systems.
The World Meteorological Organization stated soil mois-
ture as one of the key climatic variables. Routine soil test-
ing can determine the salinity levels in soil and recommend
actions to rectify the specific salinity problem in the ground.
With an increase in salinity of soils, plants are less capable
of getting as much water from the soil. The pH, electrical
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10. 10 SELVARAJ ET AL.
F I G U R E 9 Interfacing circuit of Raspberry Pi with soil moisture and salinity measurement. ADC, analog to digital converter; GPIO,
general-purpose input–output; SCL, serial clock line; SDA, serial data line; VDD, voltage drain drain; VSS, voltage source source
conductivity, and water-soluble levels of the soil are deter-
mined by this experiment. This research suggested a tai-
lored electrical sensor to detect soil moisture using resis-
tive and conductive characteristics to solve problems in the
paddy field. The system developed for experimental analysis
is given below.
The unique hardware configuration in Figure 9 is built
using high-precision ADC MCP 3008 with a 200kSPS sam-
pling rate. It is an 8 bit 10, channel ADC with I2C protocol; it
is the most suitable analog interface for the RPi platform. The
ADC is configured with software SPI protocol. Hardware SPI
is less flexible and works with specific pins of RPi. Land con-
ductivity and resistivity are tested using 5-cm apart copper
plates 25-cm long. The salinity of the water is a critical met-
ric to consider when attempting to gauge its overall quality.
Water’s salinity is determined by the number of salts that have
been dissolved. Typically, this quantification is computed by
dividing or parts per million. The water’s conductivity has
been measured with a network that helps or contacts the per-
meability sensor to get this value. One plate (P1) is driven by
DC voltage, and the voltage measure on Plate P2 is directly
proportional to soil conductivity in both salt and water con-
tent. The ADC’s output is connected to GPIO 4 of RPi; this
I2C protocol configures the sensor node as a slave. Figure 9
shows how to set up the ADC’s SPI protocol as the best ana-
logue interface for the RPi platform. Because the RPi has only
specific pins for SPI, hardware SPI is the only option. Salt and
water content directly affect the voltage measured on Plate P2.
The RPi’s GPIO 4 is connected to the ADC’s output, and the
sensor node is configured as a slave in the I2C protocol. The
moisture content in soil is determined using the equation:
𝑀c =
(
𝑊m_soil − 𝑊d_soil
)
∕𝑊d_soil (5)
Here, Wm_soil is the weight of moist soil and Wd_soil is the
weight of dry soil taken from the field. Furthermore, the water
content in soil is calculated using the formula given below.
𝑊depth = 𝑅b_dens
[
𝑊percent
100
]
𝑆depth (6)
In Equation 6, Rb_dens, Wpercent and Sdepth representing
relative bulk density, percentage of water content, and soil
depth, respectively. It is necessary to use an analog-to-digital
converter MCP3008 to read the voltage on the RPi. The
voltage can be calculated using the following formula:
𝑉out =
(
ADCout∕1, 023
)
𝑉in (7)
In Equation 7, the value Vout will depend on the input volt-
age of the sensor Vin, here Vin is set with 5 volts. The res-
olution of ADC MCP 3008 is 10 bits, and for conversion,
ADC output is divided with a value of 1,023. The experimen-
tal setup for soil surface moisture is approximately 10–15 cm,
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11. SELVARAJ ET AL. 11
and the root-soil water is monitored using long copper rode
according to the plants up to 200 cm. The proposed frame-
work measures air humidity coupled with soil moisture and
salinity and is displayed in the firebase console. In general,
the decision for irrigation relies on soil moisture content. The
upper humidity is set to 90 and 75% as the lower humidity
to ensure error-free operation. The designed irrigation water
depth is given below.
Ir g_depth = Sm (𝑖) (8)
In Equation 8, Ir g_depth is the required irrigation depth in
centimeters and Sm(i) is the soil moisture measured at ith an
instant of irrigation. For the précised irrigation, the amount of
water required is defined as:
IrVol = 0.1 × Sm (𝑖) × Sd × Pw (Ul − Ll)∕ξ (9)
Here, I rVol is the required volume of irrigation in millime-
ters, Sd is soil density, Pw is a percentage of wet soil, ξ is
coefficient of drip irrigation, Ul and Ll is the upper and lower
irrigation level at ith instant, respectively. By installing this
design, every interval detected parameter is updated, and if
any of the earlier parameters are substantially changed, they
are instantly notified by message. One of the disadvantages
of the previous method was measuring the moisture in topsoil
layers; it was addressed by using this long copper rod experi-
ment.
3.2.4 Wind speed sensor
This section examines the effect of wind and rain on agri-
cultural farming and crop production using IoT sensors. The
wind requirement changes depending on the crop type, and
wind direction and velocity have a considerable effect on crop
growth. For example, wind enhances ethylene production and
nitrogen concentration in barley (Hordeum vulgare L.) and
rice (Oryza sativa L.), lowering the rice’s gibberellic acid
level.
As shown in Figure 10, the developed equipment is evalu-
ated for the laboratory’s best adjustment and calibration pro-
cedure. Anemometer is the right instrument for measuring
wind speed; this research proposes a customized, low-cost
tool using an SMPS/PC Fan. A PC fan is brushless, and
when the rotation speed varies, it produces electrical AC volt-
age. It has a rotation per minute (RPM) of 2,000–2,500 at
12 V-0.5 amperes. The digital signal oscilloscope shows the
AC voltage generated by the fan in real time. The rotation
speed changes by altering the power signal and calibrating
between 1 and 12 Volt DC input voltage. Corresponding volt-
ages from the wind gauge are measured in digital multime-
ter with maximum accuracy. The following graph explores
F I G U R E 1 0 Laboratory set-up for the measurement of the wind
velocity and AC voltage output. (1) Tektronix TBS11023B/100Mhz
Digital Signal Oscilloscope. (2) Wind speed measurement using PC
fan. (3) Keithley 223A 30V/2Atripple channel power supply. (4)
Supporting stand. (5) Sonel CMM-11 Digital multimeter
F I G U R E 1 1 Rotation per minute (RPM) vs. AC output voltage
of wind gauge sensor
the correlation between RPM and the voltage in laboratory
configuration.
In Figure 11, the graph shows the speed of rotation (RPM)
vs. output voltage ratio. The AC voltage from the fan output
is connected to the MCP3008 ADC’s first channel (A0). The
voltage generated by the fan is proportional to the RPM and is
converted to wind speed with certain programming methods.
The three control signals master in slave out (MISO), master
out slave in (MOSI), and serial clock (SCK) signals provided
the synchronization between MCP3008 and RPi.
A motor can be used quite well as a speed sensor, despite
the practical problem of not calibrating it right. The motor’s
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12. 12 SELVARAJ ET AL.
F I G U R E 1 2 Interfacing circuit of
Raspberry Pi (RPi) with wind speed
measurement system. ADC, analog to digital
converter; GPIO, general-purpose input–output;
SCL, serial clock line; SDA, serial data line;
VDD, voltage drain drain; VSS, voltage source
source
internal resistance does not influence the response of the out-
put because this shifts the ratio of the voltage divider, which
gets canceled out by the “zero” value. Generally, calibrating
this setup gives some problems, then turning a motor into a
wind speed sensor works quite well.
In Figure 12, ADC MCP3008 is used to measure wind sen-
sor voltage, and it acts as an interface between RPi and sensor.
Naturally, an analogue output does not immediately correlate
with wind speed. Therefore, a correlation function is applied
to link analogue inputs to real-world data by sampling the
data for 10–15 min. The equation below shows the correla-
tion function of speed and output voltage of the wind sensor.
𝑌0 − 𝑌1 = 𝑀𝑋0 − 𝑋1 (10)
In Equation 10, M is the slope of input and output, Xi and Yi
are the analogue output voltage and speed of rotation, respec-
tively.
The experimental setup and results in Figure 13 showed
that a CPU fan could detect the wind speed accurately without
any further hardware changes and with an appropriate tuning
mechanism. Equation 10 is correlated speed and output volt-
age of the wind sensor. Wind direction and speed affect crop
growth significantly. Wind promotes atmospheric turbulence,
boosting the availability of carbon dioxide to plants, resulting
in higher levels of photosynthesis. The wind affects the hor-
mone balance and promotes ethylene generation in crops like
rice and barley.
F I G U R E 1 3 Real-time wind speed readings for a period of
March to June 2021
3.2.5 Rain gauge sensor
The rain gauge is built using a Hc Sr 04 ultrasonic sensor. It is
a noncontact sensor with a 2 mm accuracy range of 2–400 cm.
The Hc Sr 04 sensor is ideal for monitoring the rain gauge. The
experimental setup has a 5-cm radius glass tube; the distance
between sensor and water decreases if tube water rises. The
timing diagram of this process is shown in Figure 14.
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13. SELVARAJ ET AL. 13
F I G U R E 1 4 (a) Timing diagram of Hc SR 04 Ultrasonic Sensor (Ref. HC-SR04 datasheet). (b) Interfacing circuit of Raspberry Pi with rain
gauge sensor using Hc SR 04. GPIO, general-purpose input–output; SCL, serial clock line; SDA, serial data line; VDD, voltage drain drain; VSS,
voltage source source
To start measuring, the SR04’s Trig and pin must receive
a high pulse for 10 us. The sensor will then fire an eight-
cycle ultrasonic burst at 40 kHz and wait for the reflection.
Any ultrasonic detected by the sensor raises the Echo pin and
delays proportionally. To find the distance, measure the signal
width at the echo pin.
Sound travels through air at roughly 344 m s–1; there-
fore, multiply the time it takes for the sound wave to
return by 344 to obtain the total round-trip distance. Divide
the round-trip distance in half to get the distance to the
item.
Distance = (Speed of sound × time) ∕2 (11)
The formula for the speed of sound in the air when temper-
ature and humidity are taken into consideration is:
𝐶 = 331.4 + (0.606𝑇 + 0.0124𝐻) (12)
where C is the sound speed in meters per second, at 0 ˚C and
0% humidity, the speed of sound is 331.4 m s–1. The letter T
stands for temperature, and H denotes the relative humidity
percentage. The quantity of water in the tube is represented
by the distances calculated by Equation 5. This value denotes
the unit of rainfall in that geographical region. The physical
and geographical factors listed above will help farmers decide
and take measures on specific agricultural farming.
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14. 14 SELVARAJ ET AL.
ALGORITHM
Smart irrigation using sensors and IoT platform
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15. SELVARAJ ET AL. 15
F I G U R E 1 5 The proposed architecture of the Quadrotor unmanned aerial vehicles (Q-UAV) filed monitoring system. APN, AWS Partner
Network; PDA, personal digital assistant; XMPP, extensible messaging and presence protocol
Algorithm 1 has been scheduled into three steps: initializa-
tion and atmospheric and soil parameters measurement. While
the system is initializing, it will set the threshold values for
each sensor following the settings that have been made. If the
mode is manual, the system will wait for commands from the
user’s side, which is received over the cloud interface. When
in automatic mode, the system will read the I2C data from the
GPIO4 line and extract the temperature and humidity values
from the data stream using MACADDR. Because of the intel-
ligence provided to the RPi through the Python IDLE, the
smart irrigation system has been controlled efficiently with-
out manual intervention. The IDLE identified the DS18B20
and DHT11 using the starting address 28xx:yy: and 84xx:yy:
respectively. Equations 1.1 and 1.2 are used to read the real-
time temperature TRT and humidity HRT. When the relative
humidity is greater than or equal to threshold humidity (HRT ≥
HTH) and (TRT ≥ TTH) the drip irrigation has been activated by
RPI. The soil parameters are interfaced with the cloud server
through an SPI line and are stored on the cloud server. Farm-
ers can get this information through smartphones and personal
digital assistants. In addition, farmers can use their cell phones
to monitor and regulate the status of the motor.
4 UNMANNED ARIAL VEHICLES FOR
REAL-TIME AGRICULTURE FIELD
MONITORING
Because of the changes in the agriculture industry, Quad-
copter unmanned aerial vehicles (UAVs) have emerged as one
F I G U R E 1 6 Real-time smart irrigation and monitoring system
using Internet of Things and Quadrotor unmanned aerial vehicles
architecture. (1) 12V/125Watt solar panel. (2) 12v/7Ah battery. (3) RPI
4B computer. (4) DHT 11 sensor. (5) Battery charge indicator.
(6) DS18B20 sensor. (7) Wind gauge. (8) Soil salinity and moisture
sensor. (9) Supporting stand
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16. 16 SELVARAJ ET AL.
TA B L E 1 DHT11 measurement vs. existing sensor
SI no. Dorigin Dresearch Perror
1 85.56 84.94 0.72
2 87.11 87.9 0.89
3 82.45 81.38 1.29
4 92.67 91.9 0.83
5 89.1 90.21 1.23
6 93.44 93.88 0.46
7 87.58 87.9 0.36
8 84.55 84 0.65
9 86.98 86.33 0.74
10 83.52 83.89 0.44
Note. Dorigin, original data; Dresearch, research data; Perror, percentage of error; SI,
serial number.
TA B L E 2 DS18B20 temperature vs. mercury-based sensor
SI no. Dorigin Dresearch Perror
1 33.23 32.89 1.02
2 31.45 31.32 0.41
3 31.98 31.88 0.31
4 33.55 33.05 1.49
5 32.12 31.95 0.53
6 32.94 32.89 0.15
7 34.21 33.58 1.84
8 33.98 33.45 1.56
9 33.94 33.32 1.83
10 33.58 33.11 1.4
Note. Dorigin, original data; Dresearch, research data; Perror, percentage of error; SI,
serial number.
of the most widely recognized and fascinating technologies
in the world right now. Farmers are using smart sensors, and
UAVs are always required to obtain accurate and up-to-date
information about crop health and the presence of insecti-
cides in remote areas. It is an essential part of their business,
especially in large farming areas and small-scale operations.
The proposed vision-based Q-UAV for IoT applications is dis-
cussed in the following section.
Implementation of the proposed Q-UAV architecture with
RPi as the central controlling station for computer vision and
flight assistance is shown in Figure 15. The Q-UAV used for
this research and RPi unit will have made it possible for real-
time HD image and video streaming to the Firebase cloud.
The UAV is installed with RPi is, an eight-megapixel cam-
era serial interface camera. The end-user can view the real-
time video through an application installed on their Android
mobile device, as well as from a cloud database accessed
remotely through the IoT. The IoT can offer new cost and
process advantages to machine vision and open the way for
TA B L E 3 Proposed wind speed vs. anemometer
SI no. RPM Dorigin Dresearch
1 154 0.79 0.82
2 250 1.29 1.34
3 310 1.63 1.7
4 445 2.27 2.36
5 520 2.65 2.76
6 639 3.36 3.5
7 734 3.84 4
8 850 4.34 4.52
9 945 4.82 5.02
10 1,056 5.39 5.61
Note. Dorigin, original data; Dresearch, research data; RPM, rotation per minute; SI,
serial number.
TA B L E 4 Proposed soil moisture vs. existing machine
SI no. Dorigin Dresearch Perror
1 45 47 4.26
2 47 48 2.08
3 53 55 3.64
4 58 60 3.33
5 64 65 1.54
6 49 51 3.92
7 65 66 1.52
8 48 49 2.04
9 66 67 1.49
10 59 59 0
Note. Dorigin, original data; Dresearch, research data; Perror, percentage of error; SI,
serial number.
the integration of computer vision into inspection systems.
Using UAVs, conventional agricultural techniques may close
the gap left by human error and inefficiency. The Q-UAVs
are expected to provide a variety of services. It is more chal-
lenging to obtain aerial imagery; manned aircraft or satellites
deliver the message. The Q-UAV technology is eliminated all
ambiguity or guessing and concentrates on precise and trust-
worthy information.
Earlier, remote monitoring and sensor data interfacing were
restricted. Because advancing with embedded interfaces and
IoT in SoC chips, remote data monitoring has been feasible
using cloud services like Thingspeak, Firebase, and the pri-
vate cloud. Due to broad communication protocol support
and video interfacing with operating system support, RPi is
chosen as the central controller for UAV and sensor interfac-
ing. The data from sensors send to google firebase using RPi,
DS18B20, DHT11 and wind speed, soil salinity, and moisture.
Besides this, images of files collected by Q-UAVs are sent to
the cloud console without delay.
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17. SELVARAJ ET AL. 17
F I G U R E 1 7 Experimental results for observed and actual data: (a) humidity and (b) temperature
F I G U R E 1 8 Experimental results for observed and actual data: (a) wind speed and (b) soil moisture
5 RESULTS AND DISCUSSION
In this research, the primary goal is to collect the physical
parameters of a farming land using sensors and to use the data
collected by the sensors, along with live images from the land,
to develop a challengeable framework for smart agricultural
fields. When combined with computer vision, this architec-
ture improves the accuracy of soil and atmospheric parame-
ter measurement. As demonstrated in the experiment, a good
estimation of the soil as mentioned above parameters with a
Q-UAV system can be used for optimum irrigation with effi-
cient natural rain and resources use.
As described in Section 3, a field data collection node
has been deployed in the agricultural land with one unit
for 0.2 ha of land. The data is collected at a cloud server
using the web services, and this data is analyzed using the
farmers on their smartphones. Further, a responsive firebase
cloud interface has been developed for real-time monitoring,
data visualization, decision support, and drip irrigation
scheduling.
A photograph of a real-time monitoring and irrigation sys-
tem with a solar power backup is shown in Figure 16. The
newly built system with low power consumption and envi-
ronmentally friendly architecture has been implemented. The
proposed technology is tested in various fields to determine its
suitability and accuracy for practical application. The imple-
mentation considers some of the essential parameters listed
above, and they are computed both on analog sensors and on a
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18. 18 SELVARAJ ET AL.
cloud platform. The following equation illustrates error analy-
sis by comparing research data from the proposed system and
the original data.
𝑃error =
[
𝐷origin − 𝐷research
𝐷origin
]
100 (13)
In Equation 13, research classifies data collected by sen-
sors as Research data (Dresearch) and original data (Dorigin).
The suggested system’s performance is tested numerous times
to assure correctness. The next part analyzes this research’s
accuracy and error (Perror) in depth. The next sections exam-
ined the research output for research parameters using exist-
ing meters and the proposed method, together with their error
levels.
Tests measuring humidity using existing equipment and the
proposed approach are shown in Table 1, along with a percent-
age of detected errors. Observations from the preceding inves-
tigation have revealed that the average error probability Perror
is 1.09%, and the maximum error is up to 1.29%. The results
indicated that 98.71% of the accuracy of the developed hard-
ware had been obtained after being verified in various fields.
Table 2 shows the results of the temperature measurement
using the DS18B20 sensor. To ensure the correctness of the
proposed system, the performance of the proposed system is
tested numerous times. The difference between actual data
and research data is investigated and presented. It has achieved
a notable accuracy of 98.16%, which is impressive.
Table 3 shows the results of tests detecting wind speed
in km h–1, using anemometers and the suggested system,
together with their error values. The accuracy of this research
obtained as 94.39%, while the average error is 3.16%. The pro-
duced system’s efficacy is fulfilled with the least amount of
design work and the lowest possible cost.
There is 95.74% efficacy of soil moisture in this experi-
ment, and this research output is sufficient to meet the goals
of this research. Among other things, the Perror at differ-
ent observations for different parameters such as humidity,
temperature, wind speed, and soil moisture are displayed
in each of the four tables in the preceding part of this
paper.
Temperature is calculated in degrees Celsius; humidity
is reported in percentage units. Tables 1 and 2 computed
the Perror of each parameter using Equation 13. It has been
observed that the average real-time humidity error is 1.09%
and maximum error up to 1.29%. Table 2 and Figure 17b
shows that the temperature sensor has 0.68 and 1.29% of aver-
age and maximum error, respectively.
In general, wind speed is expressed in kilometers per hour
(km h–1). The percentage of wind velocity and soil mois-
ture errors is shown in Tables 3 and 4 and visually shown in
Figure 18. It produces an average error of 3.93 and 2.65% and
a maximum error of 4.11 and 4.35.
F I G U R E 1 9 Experimental results plotted with percentage of
error (Perror)
The foregoing study has concluded that the proposed
method has less risk of error and offers a high level of pre-
cision and feasibility than any irrigation management system
using IoT and sensors (Figure 19). Afterward, the data can
be evaluated and securely saved in the firebase cloud, where
authorized persons or farmers can access it at any moment for
additional purposes. The developed system operates in two
modes, namely, automatic mode and manual mode. Choos-
ing an irrigation system in manual mode is dictated by the
user’s assessment of the projected soil and atmospheric con-
ditions. In addition, the user can monitor the fields using
the Q-UAV through a cloud-based interface. While in auto
mode, a user can specify the threshold values for tempera-
ture, soil moisture, salinity, and other variables. The system
automatically adjusts the irrigation depending on the sensor
information. The proposed system is as smart as the pre-
cision of the sensor measurements it uses when it comes
to smartness. Hourly field data for temperature, humidity,
soil moisture, and wind speed will be collected for 4 mo
from March to June 2021 to evaluate the accuracy of the
model’s prediction. In Section 3.2, hourly data for the previ-
ous 4 mo has been averaged each day, and this information is
discussed.
6 CONCLUSION
The environmental factors, including temperature, humidity,
wind speed, soil moisture salinity, are the most significant
parameters in a smart irrigation system. With improvement
in technology, a UAV-based monitoring system will lower
the labor cost and enhance crop yield by taking real-time
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19. SELVARAJ ET AL. 19
decision-making mechanisms. This designed framework with
smart sensors and computer vision has accurately monitored
the ambient factors. These values are available in the user
front end and the firebase console without IoT architecture
latency. The open-source software and RPi platform is the
best ideal for IoT and AI applications; it decreases the man-
ufacturing and maintenance cost of the product generated.
The auto mode setup makes the smart irrigation system more
flexible to the farmers and innovative. The average variation
between real data and observed humidity, temperature, wind
speed, and soil moisture is 1.09, 0.68, 3.93, and 2.65%, respec-
tively. These results and the feasibility thought of the sug-
gested strategy are presented. This irrigation framework and
Q-UAV design can be deployed in any farming sector like live-
stock and plants with solar power.
AC K N OW L E D G M E N T S
The authors would like to thank Department of Science and
Technology (DST), New Delhi, India, for the funding to carry
out the Research work - DST/TDT/AGRO-20/2019 & 22-01-
2020 from Karpagam Academy of Higher Education, Coim-
batore, India, and the dataset collection has been supported
from Botswana International University of Science and Tech-
nology (BIUST), Botswana.
AU T H O R C O N T R I B U T I O N S
Rajalakshmi Selvaraj: Conceptualization; Methodology;
Writing – original draft; Writing – review & editing. Venu
Madhav Kuthadi: Software; Validation. S Baskar: Data
curation; Formal analysis
C O N F L I C T O F I N T E R E S T
The authors declare no conflicts of interest.
O RC I D
Rajalakshmi Selvaraj https://orcid.org/0000-0002-5571-
5316
R E F E R E N C E S
Albuquerque, C. K., Polimante, S., Torre-Neto, A., & Prati, R. C. (2020).
Water spray detection for smart irrigation systems with Mask R-
CNN and UAV footage. In 2020 IEEE International Workshop on
Metrology for Agriculture and Forestry (MetroAgriFor) (pp. 236–
240). IEEE.
Allred, B., Martinez, L., Fessehazion, M. K., Rouse, G., Williamson,
T. N., Wishart, D., Koganti, T., Freeland, R., Eash, N., Batschelet,
A., & Featheringill, R. (2020). Overall results and key findings on
the use of UAV visible-color, multispectral, and thermal infrared
imagery to map agricultural drainage pipes. Agricultural Water
Management, 232, 106036. https://doi.org/10.1016/j.agwat.2020.
106036
Bu, F., & Wang, X. (2019). A smart agriculture IoT system based on
deep reinforcement learning. Future Generation Computer Systems,
99, 500–507. https://doi.org/10.1016/j.future.2019.04.041
Canales-Ide, F., Zubelzu, S., & Rodríguez-Sinobas, L. (2019). Irrigation
systems in smart cities coping with water scarcity: The case of Valde-
bebas, Madrid (Spain). Journal of Environmental Management, 247,
187–195. https://doi.org/10.1016/j.jenvman.2019.06.062
Cao, P., Liu, Y. I., Yang, C., Xie, S., & Xie, K. (2019). MEC-driven UAV-
enabled routine inspection scheme in wind farm under wind influence.
IEEE Access, 7, 179252–179265. https://doi.org/10.1109/ACCESS.
2019.2958680
Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020). Secu-
rity and privacy in smart farming: Challenges and opportunities.
IEEE Access, 8, 34564–34584. https://doi.org/10.1109/ACCESS.
2020.2975142
Kataev, M. Y., & Dadonova, M. M. (2019). Farm fields UAV images
clusterization. In 2019 International Multi-Conference on Engineer-
ing, Computer and Information Sciences (SIBIRCON) (pp. 1011–
1012). IEEE.
Li, S., & Fang, X. I. (2021). A modified adaptive formation of UAV
swarm by pigeon flock behavior within local visual field. Aerospace
Science and Technology, 114, 106736. https://doi.org/10.1016/j.ast.
2021.106736
Liao, R., Zhang, S., Zhang, X., Wang, M., Wu, H., & Zhangzhong,
L. (2021). Development of smart irrigation systems based on real-
time soil moisture data in a greenhouse: Proof of concept. Agri-
cultural Water Management, 245, 106632. https://doi.org/10.1016/j.
agwat.2020.106632
López, C. D., & Giraldo, L. F. (2019). Optimization of energy and water
consumption on crop irrigation using UAVs via path design. In 2019
IEEE 4th Colombian Conference on Automatic Control (CCAC) (pp.
1–5). IEEE.
López, A., Jurado, J. M., Ogayar, C. J., & Feito, F. R. (2021). A frame-
work for registering UAV-based imagery for crop-tracking in Preci-
sion Agriculture. International Journal of Applied Earth Observation
and Geoinformation, 97, 102274. https://doi.org/10.1016/j.jag.2020.
102274
Mahbub, M. (2020). A smart farming concept based on smart embedded
electronics, internet of things and wireless sensor network. Internet of
Things, 9, 100161. https://doi.org/10.1016/j.iot.2020.100161
Nawandar, N. K., & Satpute, V. R. (2019). IoT based low cost and intel-
ligent module for smart irrigation system. Computers and Electronics
in Agriculture, 162, 979–990. https://doi.org/10.1016/j.compag.2019.
05.027
Ogidan, O. K., & Afia, K. R. (2019). Smart irrigation system with an
android-based remote logging and control. In 2019 IEEE AFRICON
(pp. 1–4). IEEE.
Podder, A. K., Bukhari, A. A. L., Islam, S., Mia, S., Mohammed, M.
A., Kumar, N. M., Cengiz, K., & Abdulkareem, K. H. (2021). IoT
based smart agrotech system for verification of urban farming param-
eters. Microprocessors and Microsystems, 82, 104025. https://doi.org/
10.1016/j.micpro.2021.104025
Rahman, M. F. F., Fan, S., Zhang, Y., & Chen, L. (2021). A com-
parative study on application of unmanned aerial vehicle sys-
tems in agriculture. Agriculture, 11(1), 22. https://doi.org/10.3390/
agriculture11010022
Reghukumar, A., & Vijayakumar, V. (2019). Smart plant watering sys-
tem with cloud analysis and plant health prediction. Procedia Com-
puter Science, 165, 126–135. https://doi.org/10.1016/j.procs.2020.01.
088
Rodriguez-Galvis, J., Angulo-Morales, V., Gaona-García, E., &
Lizarazo-Salcedo, I. (2020). Development of low-cost ground
14350645,
2023,
1,
Downloaded
from
https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061
by
INASP/HINARI
-
BOTSWANA,
Wiley
Online
Library
on
[16/02/2023].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
Wiley
Online
Library
for
rules
of
use;
OA
articles
are
governed
by
the
applicable
Creative
Commons
License

20. 20 SELVARAJ ET AL.
control system for UAV-based mapping. In IGARSS 2020-2020 IEEE
International Geoscience and Remote Sensing Symposium (pp. 5111–
5114). IEEE.
Serdaroglu, K. C., Onel, C., & Baydere, S. (2020). IoT based smart plant
irrigation system with enhanced learning. In 2020 IEEE Comput-
ing, Communications and IoT Applications (ComComAp) (pp. 1–6).
IEEE.
Shi, Y., Bai, M., Li, Y., & Li, Y. (2018). Study on UAV remote sens-
ing technology in irrigation district informationization construction
and application. In 2018 10th International Conference on Measuring
Technology and Mechatronics Automation (ICMTMA) (pp. 252–255).
IEEE.
Tiglao, N. M., Alipio, M., Balanay, J. V., Saldivar, E., & Tiston, J.
L. (2020). Agrinex: A low-cost wireless mesh-based smart irriga-
tion system. Measurement, 161, 107874. https://doi.org/10.1016/j.
measurement.2020.107874
Zhu, X., Zhu, X., Yan, R., & Peng, R. (2021). Optimal routing, aborting
and hitting strategies of UAVs executing hitting the targets consid-
ering the defense range of targets. Reliability Engineering & System
Safety, 215, 107811.
How to cite this article: Selvaraj, R., Kuthadi, V. M.,
& Baskar, S. (2023). Real-time agricultural field
monitoring and smart irrigation architecture using the
internet of things and quadrotor unmanned aerial
vehicles. Agronomy Journal, 115, 1–20.
https://doi.org/10.1002/agj2.21061
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