Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation

2022 IEEE 2nd
Mysore Sub Section International Conference (MysuruCon)
978-1-6654-9790-9/22/$31.00 ©2022 IEEE 1
Implementation of IoT in Agriculture: A Scientific
Approach for Smart Irrigation
Viswanatha V
Electronics and communication
Engineering ,
Nitte Meenakshi Insitute of Technology
Bangalore,Karnataka, India
Ashwini Kumari P
Electrical and Electronics
Engineering,
REVA University,
kanchanapu@gmail.com
Ramachandra A.C SMIEE
Engineering
Nitte Meenakshi Insitute of Technology
ramachandra.ac@nmit.ac.in
Srinivasa Murthy R
Engineering,
Bangalore Institute of Technolgy,
srinivasamurthyr01@gmail.com
Venkata Siva Reddy R
Engineering
REVA University
rvsreddy2007@gmail.com
Sathisha B M
Engineering, Nitte Meenakshi Insitute
of Technology,
sathisha.bm@nmit.ac.in
Abstract— Digital technologies empower the
transformation into data-driven, intelligent, agile, and
autonomous farm operations and are typically considered a
key to addressing the grand challenges in agriculture. To avoid
unscientific water supply for plantation as well as to save the
water and also yield the better crop, therefore, to increase
production efficiency out of smart irrigation and to send the
status of irrigation at standard environmental conditions, The
Internet of Things (IoT) based prototype is designed and
implemented. The prototype automatically turns ON /OFF the
motor pump based on the moisture level of the soil by taking
the temperature and humidity of the environment near the
plantation into consideration (In India, the standard
parameters for watering the vegetable plantation are
Humidity>60%, Temperature < 25°C and Humidity<40% ).
The prototype is designed with an ESP32S microcontroller
with DHT 11 and a moisture sensor. Arduino IDE development
tool is used for programming ESP32S using embedded C
programming language. The prototype is configured,
programmed, and connected to the Arduino IoT cloud. The
data of temperature, humidity, and moisture are received via
message queuing telemetry transport (MQTT) protocol on IoT
cloud through public IP therefore the data can be accessed
worldwide. The authorized person can access the data and
control the motor pump from anywhere across the world. The
test data obtained out of the prototype over the cloud and at
the system are presented in the result section.
Keywords— Smart irrigation, IoT Cloud, Moisture sensor,
Temperature sensor, MQTT, Cloud Computing
I. INTRODUCTION
Smart farming involves the addition of the latest
technologies like artificial intelligence (AI) and machine
learning (ML) with IoT cloud into the existing practices in
farming. This results in increasing the production of crops as
well as efficiency in the management of farming. There are
many benefits out of using IoT in farming. IoT in farming
finds the effective use of equipment, employees, and whole
business in general since IoT systems are capable to track
the whole Business in general in farming. In fact, this kind
of technology in farming reduces the faults in production
and
Predicts the output of production. This of course allows for
having plan better distribution [1].When we are able to
predict how much we are going to harvest from farming
then there is no scope for lying around unsold things of
crops and finding the anomalies in the growth of crops [2].
This again results in mitigating the issues connected with
losing the yields of crops. Using AI and ML with IoT in
agriculture automates multiple processes across the
production stage. For example smart water irrigation, smart
disease detection of the plantation, livestock, and real-time
prediction of growth of plantation. All these things lead to
high standards of quality of crops via automation.
Eventually, all these techniques help in farming to have
higher revenue [3].
Internet of things basically refers to the typical physical
devices in agriculture are microcontrollers (The typical
microcontroller like ESP32 has built Wi-Fi and classic
Bluetooth), Sensors like temperature and humidity sensors,
moisture sensors and finally, actuators like a buzzer, relay,
and led and so on are connected together forms perception
layer for sharing data among themselves and sending the
data to the next layer depends on the architecture of IoT
chosen while designing the system. The main objective of
using IoT is to ease up everyone’s tasks if they are complex
in different perspectives [4].Therefore having devices
around us that are capable of having networking features
can form a system that is smart enough to serve the
application for that matter [5]. In the ecosystem of IoT, there
is no standard or single architecture to be followed. Each
organization uses its own protocols across the world
therefore IoT is not gaining its own importance just because
of the issue mentioned above. The reason behind that is each
user, each company, or organization has its own
requirements. Therefore based on the requirements the
2022
IEEE
2nd
Mysore
Sub
Section
International
Conference
(MysuruCon)
|
978-1-6654-9790-9/22/$31.00
©2022
IEEE
|
DOI:
10.1109/MysuruCon55714.2022.9972734
Authorized licensed use limited to: Nitte Meenakshi Institute of Technology. Downloaded on December 14,2022 at 05:10:59 UTC from IEEE Xplore. Restrictions apply.

2
ecosystem is divided into simple three-level architecture as
shown in figure 1 which is the basic and first level of
architecture in the ecosystem of IoT [6]. This kind of
architecture contains three layers starting from the
perception layer at the bottom and the application layer at
the top. In between these two layers, there is a network
layer. The perception layer at the bottom is the first layer as
an entry point where all physical components including
sensors present. The sensors gather the information which
will be later collected by the control unit and this control
unit apply logic and send the modulated data to the network
layer. The network layer further learns itself and passes the
modulated data received from the perception layer to the
application layer [7]. The application layer later converts the
modulated data to a form that can be readable by the end-
user or end platform.
Fig.1. Three layer architecture of IOT
The three layer architecture can be updated to five layer
architecture as shown in figure 2. The five layer architecture
is quite same as three layer architecture .The only difference
is adding three new layers such as transportation layer,
processing layer and business layer in place of network
layer. In five layer architecture, the top most layer is
business layer and the bottom most layer is perception layer.
Again the job of perception layer here is remains same while
the new layer i.e. transport layer which takes the data from
perception layer and pass it on to processing layer. The
transport layer is formed with wireless protocols such as
Bluetooth, RFID, 5G, and NFC [8]. The processing layer
does the job of storing, analyzing, processing huge amount
of data and cloud computing. At present scenario there
widely used cloud systems such as, Microsoft Azure, AWS,
ThingSpeak and Arduino IoT cloud. Once cloud computing
is done with processing layer, the modulated data received is
passed on to application layer where the user readable data
will be delivered to end users. Finally the business layer
comes into picture in case of big organization .It does the
job of monitoring large number of actuators connected at
the perception layer and pass the information about the
actuators to the end users via customer care centers.
Basically the business layer manages the whole IoT
ecosystem which includes application layer and profit
models along with maintaining the privacy of end users [9].
Fig.2. Five layer IoT Architecture
Now coming down to how this can be processed using cloud
computing-based processing as shown in figure 3, where the
data received is sent to the cloud, once data is loaded in the
cloud platform and it will be used for processing and applied
on to the various applications. This will helps to get the data
and to take any action anywhere at any time within very less
amount of processing time. There are various sectors in
which this technology can be used are home automation,
autonomous vehicles, controlling of home appliances, and
building security at office premises.
Fig.3. Cloud computing
When the system needs immediate response use the fog
computing technology which is shown in figure 4. The fog
computing technologies is based on a layered approach here
the various process such as insert monitoring, preprocessing,
and storage are accommodated within the security layer
between the physical and transport layers.
Fig.4. Fog computing in IoT
In addition to these two layers, the four new layers are
added to the security layer as shown in figure5. This will
add the advantages such as the system will be smarter or
very effective with respect to security. Now at the security
layer in addition to the physical and transport layers, there

3
few layers such as the monitoring layer, preprocessing layer,
storage layer, and security layer.
Now let us discuss the real-time example, consider a traffic
system that is built using IoT technology. Let us consider
the scenario where the ambulance has to cross the signal as
the first priority. We detect the ambulance location by
getting the data that was stored in the cloud by the sensor
implemented in that location where the ambulance is
present. This data has to be processed and the corresponding
map to be provided to make the ambulance to cross the
signal with the highest priority. The information or sensor
data collected from the sensor will be sent through the
gateway where it will be preprocessed. This data will be
stored in the cloud and sent back to the sensors as well.
With the help of the data stored in the cloud, the nearby
hospitals can create a track to reach the hospital as early as
possible. In this case, security is very essential because the
data stored.
Fig.5. Fog Architecture in IoT
Now we will discuss the various taxonomies used in
association with the Internet of Things. There are a few key
layers that are present in most of the architectures of the
Internet of Things.
This idea provides the flexibility to use the customized
systems depending on their own ideas to solve the problems,
but few of these layers are needed as fundamental layers to
build any architecture. Firstly we have the perception layer,
in this layer, we collect the information from various sensors
which are connected to the system. Secondly, we have a
processing layer here we will do the filtering operation to
finalize the data to do the analytics before sending it to the
systems for processing. We have a Communication layer to
decide the communication protocols and standards used as a
medium for sending the data from the sensors to the system.
The middleware plays an important role at this level, which
is responsible for the system operating much smoother than
earlier by integrating in the information received by the
various sensors connected to the system. This data will be
passed on to the application layer to improve the overall
performance by providing more accuracy and efficiency.
Fig.6. IoT Taxonomy
II. RELATED WORK
Andrés Villa-Henriksen [10] ,describes the internet of things
in farming. Discussed about widely used communication
protocols in the infrastructure layer and application layer
connected to smart agriculture. The wireless technologies in
infrastructure layer are IEEE 802.15.4 which is used by
ZigBee or 6LowPAN or 3GPP used by GPRS, LTE or 5G.
In application layer, the protocols such as HTTP, MQTT
and XMPP are commonly used in IoT applications in
farming. Implementing protocols is challenging since the
way they are used in each application is different just like in
vehicle to vehicle communication. The protocols supposed
to be carefully employed as they are not always compatible
and can have effect on data formats or sensors and gateways
used. To simplify such issues we have middleware
platforms which can integrate the diverse protocols by
offering abstraction levels.
Chatzikostas, G., et al. [11] explains the smart agricultural
hub contains internet of food and farm. The smart hub
connects local hubs to be able to carry out high-performance
innovation experiments.
Seyyedhasani , et al. [12] infers the breaking down the fields
into paths that can be used by the vehicles which are meant
for agricultural operations therefore the efficiency of
operations get increased .Here two important routing
algorithms are used for the purpose of field division
scientifically. The algorithms are fast Clarke-Wright
heuristic and slower tabu search meta-heuristic.
Wolanin, Aleksandra, et al [13] describes the estimation of
crops in agriculture using machine learning models in
Sentinel-2 and Landsat 8 technology. The highlights of this
paper is that it conducted on 1200 scenarios via simulation
using routing algorithms. The scenarios are based on size,
shape and path generated methods.
Laghari, A.A., Wu, K., Laghari, R.A. et al. [14] explains the
importance of IoT in smart farming. Farmers can control the
water supply for plants and getting the information about
moisture level and nutrients of soil using IoT .this helps the
farmer to increase their income level by reducing cost for
farming.
A.G. El-Naggar, et al [15] infers the use of IoT with LiDAR
technology to exact estimation of crop growth and water
usage. Also find out the spatial variation in the crop growth
pattern. Terrestrial laser scanner is used for measuring crop
height and biomass of a bean crop. This method has
potential to measure the height surface of crop.
R. Venkatesan et al [16] describes the IoT verticals towards
water management in agriculture where the system can
collect the data like environmental temperature, humidity,
soil moisture level. The technology used in this paper is Wi-
Fi and ZigBee. The challenges in the current approach is
that human interaction, labour cost and wastage of water. So
the solution is to find the said parameters and control the
water automatically to save it.
III. DESIGN AND METHODOLOGY
A. Hardware
The hardware components shown in table.1 are used in the
implementation of the prototype. The approximate cost also
mentioned for reference. The complete architecture

4
designed for IoT cloud based smart irrigation system is as
shown in figure.7.
i). ESP32S: Its Microcontroller contains wireless protocols
like Wi-Fi, Bluetooth. It has sufficient GPIO pins along with
ADC channels. Therefore analog and digital sensors can be
easily interfaced with this device. It supports Embedded C
program as well as MicroPython. It is mainly used in IoT
applications like industrial automation, smart home, smart
agriculture, and IP camera and so on [17].
ii). DHT 11 sensor: Its three terminal device. Data terminal
is connected to ADC channel of microcontroller and
remaining two terminals connected to 5V-3V DC and
Ground respectively. This device is used to measures
Temperature and humidity of soil in this context. Data
terminal is connected to GPIO 15 of ESP32S device. The
GPIO 15 is basically ADC channel 13 [18].
iii). Soil Moisture Sensor: It is also having three terminals.
One is data terminal and other two are 5V DC and Ground
terminal. This is used to measure moisture level of soil. This
device is connected to GPIO 36 of ESP32s device. The
GPIO 36 is basically ADC channel 0.
iv). Submersible Mini Water Pump: Its low cost, small size
water pump operates with 5-12 V DC supply and current of
130-220mA. It can take up to 120 liters per hour with
current consumption of 220mA.while using this motor, care
should be taken to avoid dry run since it damage the motor
due to heating [19].
v). relay: The single channel relay is used to control motor
via 5V battery with 180mA current. Based on the control
signal from microcontroller, the relay can switch the battery
to submersible mini water pump [20].
vi). 5V Battery: It is used to power up the motor with
current of 130-220mA since ESP32S cannot provide the
required current for the submersible mini water pump.
vii). Wi-Fi-gateway: It is used to provide internet for the
microcontroller. In this implementation mobile device with
4G GSM is used to provide internet.
viii). Arduino IoT cloud: Its cloud platform used to
configure required things in our prototype design. In this
case, four things are configured over cloud. The things such
as Temperature, Humidity, Moisture and Motor control
.Once the configuration for things is done, Arduino cloud
will generate some default code which need to be further
edited as per our requirements. Later microcontroller board
to be connected host for programming [21].
ix).User Login: Once it is programmed successfully, we
need to look into things in cloud platform to check the
parameters like temperature and humidity as well as
moisture level. According to these parameter, motor can
turn ON and turn OFF automatically. If user wish to turn
ON or turn OFF by ignoring the parameter can also be done.
B. Software
The program logic is expressed in terms of Flowchart is as
shown in figure 8. It defines Arduino cloud library
like”thinkProperites.h”.This library support the things which
are defined by the user over the cloud. One more library is
DHT.h which supports DHT 11 device. Once all the
libraries and variables are defined, it will check for
Temperature, Humidity and Moisture level of soil. It will
turn ‘ON’ the motor only if it is satisfied with three
parameters such as Temperature is less than 30O
C ,
Humidity is more than 60% and moisture level is less than
40%.
The system controls the motor based on the condition given
in the table.1. Level of rating for moisture, temperature and
humidity are fixed based on the Indian standard for
vegetable plantation.
TABLE.I DESIGN SPECIFICATIONS
Sl.No Component Quantity Approximate
Cost
in INR
1 ESP32S 1 700
2 DHT11
Sensor
1 120
3 Soil
Moisture
Sensor
1 180
4 Submersible
Mini water
Pump
1 60
5 Relay 1 50
6 5V Battery 1 200
7 Android
Mobile as
Wi-Fi
Gateway
1 User mobile
8 Arduino
IoT cloud
1 Free
(Limited)
9 User login
for data
analytics.
1 User laptop
Fig.7. Architecture of Smart irrigation with IoT Cloud
IV. RESULTS AND DISCUSSION
The hardware setup is as shown in figure. 9 with clear
labelling of each module used in building prototype. The
porotype is tested for automatic motor control based on
moisture level of soil and temperature and humidity at
environment. The testing is done for moisture sensor in two

5
cases i).By placing sensor in a container having water to
measure the moisture level. ii). By placing the moisture
sensor outside the container having water. The prototype is
working perfectly. In India, the standard humidity and
temperature & humidity levels for vegetable plantations are
as follows: Humidity level supposed to be more than 60%,
Moisture level supposed to be more than 40 % and
Temperature supposed to be less than 30° Celsius. The
motor pump is turning ON as well as turning OFF based on
moisture level and also temperature and humidity at
environment. The automatic motor control based on
parameters and conditions are as shown in the table.2.Four
IoT things present in prototype are configured Arduino IoT
cloud as shown in figure.10. Programming is done over IoT
cloud for the prototype as shown in figure.11.The data of
moisture, temperature and humidity parameters are obtained
on Arduino IoT cloud as shown in fig.12 .The data obtained
at prototype side is as shown in figure 13.
Fig.8. Logic used in smart irrigation with IoT
Fig.9. Hardware Test Bench Setup
Fig.10. Configuration of prototype in IoT cloud
Fig.11. Programming prototype in IoT cloud
Fig.12. Data received at Arduino IoT cloud

6
Fig.13. Data obtained at prototype side
TABLE.II AUTOMATIC MOTOR CONTROL CONDITIONS
SL.N
o
Moisture
Level (%)
Temperatur
e 0C
Humidity (%) Motor Status
1 < 40 < 30 > 60 ON
2 >40 > 30 < 60 OFF
3 > 40 > 30 > 60 OFF
4 > 40 < 30 < 60 OFF
5 > 40 < 30 > 60 OFF
6 < 40 > 30 < 60 OFF
7 < 40 > 30 > 60 OFF
8 < 40 < 30 < 60 OFF
V. CONCLUSION AND FUTURE SCOPE
The rise and development of IoT and its cloud
computing feature for agriculture with tiny microcontrollers
bring prototype with cost effective as well as efficiency in
production. This system provide information correctly and
timely since this system with IoT and cloud has certain
advantages like small size, light weight, low noise, high
reliable ,speed and low maintenance cost therefore the
formers will handle it efficiently. This will be a great
development for formers. The programming is simple to
implement the applications using the Arduino IoT cloud for
the Agriculture applications wirelessly using ESP32S
microcontroller as it has wireless technologies like Wi-Fi
and Bluetooth. Besides, the prototype is stable and reliable
than the traditional ones. Its maximum speed of printing can
be up to 80mm per second. The system has highly
adaptability. It can be used in many situations not only for
smart irrigation Further this system can be extended for
internet of things (IoT) with TinyML based control with
better accuracy and efficiency .
ACKNOWLEDGMENT
It is to acknowledge that this research is funded by Nitte
Meenakshi Institute of Technology, Bangalore.
REFERENCES
[1] F. Guo, F. R. Yu, H. Zhang, X. Li, H. Ji and V. C. M. Leung,
"Enabling Massive IoT Toward 6G: A Comprehensive Survey," in
IEEE Internet of Things Journal, vol. 8, no. 15, pp. 11891-11915, 1
Aug.1, 2021.
[2] J. Ruan et al., "A Life Cycle Framework of Green IoT-Based
Agriculture and Its Finance, Operation, and Management Issues," in
IEEE Communications Magazine, vol. 57, no. 3, pp. 90-96, March
2019.
[3] Madushanki, AA Raneesha, et al. "Adoption of the Internet of Things
(IoT) in agriculture and smart farming towards urban greening: A
review." International Journal of Advanced Computer Science and
Applications 10.4 (2019).
[4] Viswanatha, V., Ashwini Kumari, and Pradeep Kumar. "Internet of
things (IoT) based multilevel drunken driving detection and
prevention system using Raspberry Pi 3." International Journal of
Internet of Things and Web Services 6 (2021).
[5] A. Vangala, A. K. Das, N. Kumar and M. Alazab, "Smart Secure
Sensing for IoT-Based Agriculture: Blockchain Perspective," in IEEE
Sensors Journal, vol. 21, no. 16, pp. 17591-17607, 15 Aug.15, 2021.
[6] B. Parvez, R. A. Haidri and J. Kumar Verma, "IoT in Agriculture,"
2020 International Conference on Computational Performance
Evaluation (ComPE), 2020, pp. 844-847.
[7] Lakhwani, K., Gianey, H., Agarwal, N., Gupta, S. “Development of
IoT for Smart Agriculture a Review”, In: Rathore, V., Worring, M.,
Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in
Expert Applications and Security. Advances in Intelligent Systems
and Computing, vol 841, (2019).
[8] Symeonaki, Eleni, Konstantinos Arvanitis, and Dimitrios Piromalis.
"A context-aware middleware cloud approach for integrating
precision farming facilities into the IoT toward agriculture 4.0."
Applied Sciences 10.3 (2020).
[9] He Jiang, Xiaoru Li, Fatemeh Safara, IoT-based Agriculture: Deep
Learning in Detecting Apple Fruit Diseases, Microprocessors and
Microsystems, 2021.
[10] Andrés Villa-Henriksen, Gareth T.C. Edwards, Liisa A. Pesonen, Ole
Green, Claus Aage Grøn Sørensen, Internet of Things in arable
farming: Implementation, applications, challenges and potential,
Biosystems Engineering,Volume 191, 2020, Pages 60-84.
[11] Chatzikostas, G., et al. "Smart agri hubs D3. 1 innovation experiment
guidelines." (2019).
[12] Seyyedhasani, Hasan, Joseph S. Dvorak, and Eric Roemmele.
"Routing algorithm selection for field coverage planning based on
field shape and fleet size." Computers and Electronics in Agriculture
156 (2019): 523-529.
[13] Wolanin, Aleksandra, et al. "Estimating crop primary productivity
with Sentinel-2 and Landsat 8 using machine learning methods
trained with radiative transfer simulations." Remote sensing of
environment 225 (2019): 441-457.
[14] Laghari, A.A., Wu, K., Laghari, R.A. et al. A Review and State of Art
of Internet of Things (IoT). Arch Computat Methods Eng 29, 1395–
1413 (2022).
[15] A.G. El-Naggar, B. Jolly, C.B. Hedley, D. Horne, P. Roudier, B.E.
Clothier,The use of terrestrial LiDAR to monitor crop growth and
account for within-field variability of crop coefficients and water use,
Computers and Electronics in Agriculture,Volume 190, 2021.
[16] R. Venkatesan and A. Tamilvanan, "A sustainable agricultural system
using IoT," in International Conference on Communication and
Signal Processing (ICCSP), 2017.
[17] Viswanatha, V., and R. Venkata Siva Reddy. "Digital control of buck
converter using arduino microcontroller for low power applications."
2017 International Conference On Smart Technologies For Smart
Nation (SmartTechCon). IEEE, 2017.
[18] Viswanatha, V., and R. Venkata Siva Reddy. "Research on state space
modeling, stability analysis and PID/PIDN Control of DC–DC
converter for digital implementation." Advances in Electrical and
Computer Technologies. Springer, Singapore, 2020. 1255-1272.
[19] Viswanatha, V. "Stability and Dynamic Response of Analog and
Digital Control loops of Bidirectional buck-boost Converter for
Renewable Energy Applications." International Journal of Recent
Technology and Engineering , Volume-8 Issue-2, July (2019).
[20] Nayak, J. P., Anitha, K., Parameshachari, B. D., Rashmi, P. (2017,
August). PCB Fault detection using Image processing. In IOP
Conference Series: Materials Science and Engineering (Vol. 225, No.
1, p. 012244). IOP Publishing
[21] Parameshachari, B. D., Gopy, S. K., Hurry, G., & Gopaul, T. T.
(2013). A study on smart home control system through speech.
International Journal of Computer Applications, 69(19).

Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation

More Related Content

Similar to Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation (20)

More from Springer (6)

Recently uploaded (20)

Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation