Internet of Things: Agriculture Precision Monitoring System based on

Low Power Wide Area Network

MARDENI ROSLEE1,*, TIM YAP WOON1, CHILAKALA SUDHAMANI1,

INDRARINI DYAH IRAWATI2, DENNY DARLIS2, ANWAR FAIZD OSMA3,

MOHAMAD HUZAIMY JUSOH4

1Faculty of Engineering,

Multimedia University,

63100, Cyberjaya, Selangor,

MALAYSIA

2Applied Science Faculty,

Telkom University, Bandung, 40257,

INDONESIA

3Rohde & Schwarz (M) Sdn Bhd,

40150 Shah Alam Selangor,

MALAYSIA

4Universiti Teknologi MARA,

40450 Shah Alam, Selangor,

MALAYSIA

*Corresponding Author

Abstract: - Nowadays, many people around the world depend mostly on agriculture for their livelihood. In the

majority of countries around the world, it is the most significant occupation for many families. Unfortunately,

farmers, particularly in oil palm plantations, continue to rely on age-old practices. One of the key elements in

achieving high and long-term oil palm production on peat is the adoption of efficient precision water

management. In essence, this means maintaining the water table at the necessary depth. Because of the peat's

persistently low water table, oil palm productivity has sharply decreased. In this work, an Internet of Things

(IoT) for precision agriculture monitoring is developed using a long-range wide area network (LoRaWAN)

algorithm. Based on an approach point of view, a LoRaWAN is a long-range, low-power, low-bitrate wireless

telecommunications system meant to be used as part of the Internet of Things architecture. The end devices link

to gateways through a single wireless hop using LoRaWAN. These gateways function as transparent bridges,

relaying messages from the end devices to a central network server. The ultimate result is the creation of a

precision water management assistance algorithm employing LoRaWAN and IoT that is both affordable and

effective.

Key-Words: - IoT; LoRAWAN, Wireless Communication, Sensors Networks, Topology, Water Management,

Water Table, Oil Palms.

Received: April 16, 2023. Revised: February 7, 2024. Accepted: March 8, 2023. Published: April 22, 2024.

1 Introduction

The quick development of Internet of Things (IoT)

technology and its applications has accelerated

research in various areas of industries such as

autonomous vehicles, smart cities, smart homes, and

so on. One of the most popular IoT applications is

precision agriculture.

It is predicted that there will be 9.7 billion

people on the planet by 2050, leading to a

substantial increase in the demand for food, [1]. To

address this rising need, the implementation of IoT

technology coupled with data analytics (DA) is

poised to enhance operational efficiency,

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

productivity, financial viability, and environmental

sustainability, [2], [3]. Precision agriculture

harnesses IoT technology as a vital tool for farm

management, ensuring that crop and soil data are

precisely tailored to boost productivity and meet

desired outcomes.

Traditional farming manages the crop yields

based on time-proven techniques as well as

historical regional conditions, [4], [5]. This type of

method usually causes a lot of waste such as water

and deteriorates soil quality by using significant

amounts of agrochemicals such as pesticides. This

wastage and deterioration of soil have caused a

negative impact on the profitability and the

environment. Therefore, sensor-based agriculture

was considered to reduce the impacts of traditional

farming.

Precision agriculture relies on sensors to access

the data such as temperature, soil moisture, leaf

wetness, humidity, groundwater level and so on, [6],

[7], [8]. All these sensors will be located at a

strategic location to provide the most accurate data

for analysis. The sensors use wireless connectivity

to send the environmental data to the internet cloud

for data processing and analysis. The collected data

can be used to analyze the significant patterns and

trends which will be very helpful make in making

decisions for irrigation, fertilization, pesticides, and

so on.

The sensors in IoT precision agriculture are

equipped to transmit data to the cloud, with wireless

connectivity emerging as the optimal choice, [9],

[10]. Various types of wireless technologies are

available for IoT applications, including Wi-Fi,

4G/5G, SigFox, LoRaWAN, ZigBee, and more.

Among these options, LoRaWAN stands out as one

of the most popular wireless technologies for IoT

precision agriculture due to its ability to offer long-

range communication spanning up to 20 kilometers

while maintaining low energy consumption.

Typically, it operates efficiently with just AA

batteries or Lithium-Ion batteries, providing an

extended operational lifespan, [11].

The water table represents a crucial parameter in

agriculture, as fluctuations in the water table affect

crop yields, [12]. Essentially, the water table marks

the upper boundary of the saturated zone, where the

pores and fractures within the ground contain water.

This surface coincides with the point where the

water's pressure head equals atmospheric pressure,

denoting a gauge pressure of 0. In essence, it serves

as the "surface" of the subsurface materials saturated

with groundwater in a specific area. Groundwater

can originate from precipitation or the inflow of

groundwater into the aquifer, both contributing to

fluctuations in the water table.

In regions with ample precipitation, water seeps

through soil pore spaces, traversing the unsaturated

zone. As it descends, water progressively occupies

more of the soil's pore spaces until it reaches the

saturation zone, also known as the phreatic zone.

Within this zone, permeable rock layers that yield

groundwater are referred to as aquifers. In soils with

lower permeability, like compacted bedrock

formations or historic lakebed deposits, delineating

the water table can be more challenging. Numerous

research studies have investigated the influence of

water depth on various crops, underscoring the

importance of monitoring water table fluctuations in

agriculture, [13], [14], [15], [16], [17].

In general, the water table observation wells are

located in remote areas, therefore the on-site data

collection is very time-consuming and high

manhours consumption which will lead to high cost.

In addition, due to the on-site data collection, there

is no real-time data available and decisions will not

be able to be taken immediately. The following

research questions were formed and described the

aim of this work.

 How can a LoRaWAN-based IoT node that

can measure the liquid level and visualize

the data be designed and implemented?

 How can the node be able to monitor the

battery life so that the battery can be

recharged or replaced before it runs flats?

 How to visualize the data of water level

information and battery life at the IoT

platform/dashboard?

Therefore, we considered a LoRaWAN

gateway, IoT sensor node, internet, and Blynk IoT

platform to measure the water level of a particular

crop using the water sensor and its battery level.

Smartphones are used to verify the water level and

battery level of a sensor remotely with the

smartphone and based on the water level, we can

control the water pump for half an hour.

The rest of the paper is organized as follows:

section 2 provides the literature survey, section 3

gives the scope of the work, section 4, and explains

the proposed methodology. Results, discussions, and

conclusions are drawn in sections 5, 6, and 7

respectively.

2 Literature Review

Nowadays, agriculture serves as the primary

livelihood for countless individuals across various

regions of the world. Thanks to remarkable

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

advancements in technology, farming has gained

greater popularity and significance. A diverse array

of tools and techniques are readily accessible for the

advancement of agriculture. According to the

United Nations Food and Agriculture Organization,

the world needs to produce 70% more food by 2050

than it did in 2006 to sustain the rising global

population, [18]. In response to this demand,

farmers and agricultural enterprises are increasingly

turning to the Internet of Things (IoT) for enhanced

analytics and increased production capabilities.

The IoT is a network of interconnected devices

used to communicate required data efficiently

without the need for direct human intervention.

This is more efficient because globally it can link a

large number of devices, requires less human

intervention, is quickly accessible, saves time, and

facilitates simplified communication. Nowadays,

many sectors are utilizing IoT technology. In the

field of agriculture, IoT devices increase production

by knowing the major parameters such as soil

moisture, temperature, and water level.

Hence, the agricultural sector is using IoT

technology for smart farming to improve

productivity, reach a wider audience, and cut down

on expenses, time, and human interaction. However,

monitoring of oil palms in Malaysia is one of the

major issues. In [19], Gartner analysis claims that by

the end of 2016, there were 30% more connected

devices than there were at the end of 2015. By 2020,

this number is expected to increase to 26 billion

devices.

IoT sensors provide accurate information on the

crop yields, rainfall patterns, pest infestations, and

soil nutrition to the farmers. Farmers can improve

their farming methods with the help of this essential

knowledge. The real-time, accurate, and shared

characteristics of the Internet of Things make it a

promising technology that could bring about major

changes to the agricultural supply chain. It is an

essential piece of technology for creating an

efficient agricultural logistics flow, [20].

Adopting IoT in agriculture provides several

benefits. Initially, water waste can be reduced with

the use of a water sensor, next, it permits ongoing

land surveillance, which permits the implementation

of preventative actions early on. Thirdly, IoT

improves farming operation’s efficiency and

increases productivity. Fourthly, it makes crop

monitoring easier while offering information on

crop progress. Fifth, IoT helps manage soil by

determining important factors like moisture content

and pH levels for the best possible seed selection.

Furthermore, plant and crop diseases can be

identified with the help of sensors and RFID chips.

RFID tags share information online and transfer it to

readers. Farmers and scientists can remotely access

this data, allowing for prompt crop disease

prevention, [21]. Farmers can also know the global

market prices of the crop with the use of smart

devices and increase their crop sales.

In a separate investigation documented in [22], IoT

demonstrates its versatility across various

agricultural domains, encompassing applications

such as intelligent irrigation management systems,

pest and disease control, water quality monitoring,

tracking cattle movements, dairy management,

greenhouse environmental monitoring, soil

condition assessment, and precision agriculture

employing Unmanned Aerial Vehicles (UAVs). The

cloud-based smart forming system facilitates the

early detection of borer insects in tomato crops,

[23]. This issue was effectively addressed through

the convergence of cloud computing and IoT

technologies.

Furthermore, within the scope of the paper [24], a

comprehensive architecture for monitoring soil

moisture, temperature, and humidity levels on

small-scale farms is presented. The primary impetus

behind this research is to curtail water consumption

while concurrently enhancing productivity on

modest agricultural holdings and ensuring precision

in their operations. In the paper [25], the proposed

approach leverages a fusion of LoRa technology and

cloud computing to accelerate the advancement of

agricultural modernization. This innovative

combination facilitates the creation of smart

agricultural solutions, effectively addressing the

challenges faced by farmers even in remote

locations.

Furthermore, in a separate study documented in

[26], cutting-edge communication architectures are

implemented, showcasing the integration of

foundational sensing technologies and

communication mechanisms for IoT. Additionally,

the paper delves into recent strides made in the

theory and application of wireless underground

communication. It also sheds light on the significant

hurdles inherent in IoT design and implementation.

In [27], LoRaWAN network utilization is

highlighted. This protocol offers long-distance

communication with exceptionally low energy

consumption, aligning with the growing demands

and requirements of modern farmers for enhanced

accessibility and facilities.

In [28], authors have built a prototype to

collect the air temperature, humidity, leaf wetness,

and soil moisture reading from sensors and forward

the sensed data to the things network (TTN) by

using LoRaWAN, which consists of one executor

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

node and three collector nodes. These nodes work

together to gather data, which is subsequently

analyzed to determine whether the irrigation system

should be activated or deactivated. The author has

skillfully engineered a system that offers both

flexibility and scalability, allowing for the seamless

addition of new services and integration with

various other IoT platforms.

In [29], authors have built a IoT sensor node by

using an Arduino Uno board. The sensor node

comprises 3 different types of sensors and they are

soil moisture sensor, rain sensor, and temperature

with humidity sensor. The sensor node collected the

environmental data and sent it to the Thingspeak

IoT platform via Wi-Fi. The collected data were

able to be visualized in the Thingspeak IoT

platform. The data can be made available from

smartphones which allows the user to access the

data everywhere.

In [30], authors set up an IoT system that

consists of a network and application server, sensor

node, and LoRa Gateway. The sensor node collects

temperature and humidity data and sends it to the

network server via the LoRa gateway. Network

servers receive the data from the LoRa gateway and

arrange the data set before sending it to the

application server for visualization. In the research,

the author has conducted an assessment about the

data transmission delay. The result shows that the

distance has an impact on the network connection

request, the longer the distance, the connection

request will take a longer time which causes delay.

In [31], authors have conducted experimental

research about the soybean water use, growth, and

yield parameters within the climate-controlled

greenhouse. The research was conducted under 4

different water table depths, they are 30, 50, 70, and

90 cm. The optimal depth of soybean was

determined as 70 and 90 cm.

In [32], authors developed a IoT-based Soil

Health Monitoring Unit (SHMU) with LoRaWAN

as a wireless communication medium between the

SHMU with the gateway. The node consists of a

micro-controller board, LoRa radio, soil sensors, Li-

ion battery, solar panel, and battery charger. There

are two main sensors in the SHMU and they are

commercially available soil sensors that are capable

of measuring soil EC (Electrical Conductivity),

temperature, and moisture while another sensor is

used for measuring the soil CO2 concentration. The

SHMU also has a GPS module which allows the

user to use the node GPS coordinates and mark the

sampling point on a map. Author used a personal

computer as an IoT server which consisted of

several open-source software such as ChirpStack,

Mosquitto and PostgreSQL. This software was used

for data/message processing, data storage, and

visualization. The research has shown a promising

result in which all the data were able to be collected

for visualization and storage.

In the literature, various models and methods

are considered for monitoring crop irrigation. The

authors considered sensors for monitoring the soil

moisture, temperature and humidity, co2

concentration, leaf wetness, pest infections, rainfall

patterns, and so on. In this paper, we considered a

pressure-based liquid level sensor for identifying the

water level in the ground surface, and one more

sensor is used to detect the battery level of the

network system. This battery information is one of

the major parameters we consider along with the

water level. With this device, we can control crop

irrigation without any issues like power failures and

water reduction. This provides an enhanced remote

monitoring crop irrigation system.

3 Scope of Work

The basic aim of the paper is to build a liquid-level

sensor IoT node which is used to monitor the water

table fluctuation. The battery-driven sensor node

contains a pressure-based liquid level sensor, which

is intended to measure water level with a defined

interval. Apart from the liquid level monitoring, the

sensor can monitor battery status. The collected

liquid level data and battery status will be sent to the

gateway by using LoRaWAN wireless

communication technology. Blynk IoT platform will

be used for data storage and visualization. Other

than using a PC to access the data, smartphones can

also be used to view the data from everywhere and

anytime.

4 Methodology

In the literature, many authors worked on soil

moisture sensing using various sensors and

enhanced crop irrigation. In this paper, a precision

monitoring agricultural system is considered and it

consists of sensors, a microcontroller, LORaWAN,

and current to voltage converter. Every component

is having its advantages. The proposed system

network topology, its component specifications, and

working are explained clearly in this section.

4.1 Network Topology

The proposed system's network topology is depicted

in Figure 1, which consists of four primary

components within the network architecture: the

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

sensor node, the LoRaWAN gateway, the internet,

and the IoT platform. Each component has its

importance in controlling the water level. Initially,

the sensor node detects the water level, next the

same data is forwarded to the LoRaWAN gateway,

and through the internet the sensed information is

forwarded to the Blynk IoT platform for storage and

visualization.

Fig. 1: Network Topology

4.2 Sensor Node

The sensor node is the basic element of an IoT

network topology. This node includes a 12V DC

battery, pressure-based liquid level sensor,

LoRaWAN wireless module, microcontroller, and

current to voltage converter. The water pressure

will be detected by the pressure-based liquid sensor

and the same is forwarded to the IoT platform using

the wireless module and LoRaWAN gateway. This

plays a very important role in controlling the water

level in the irrigation system.

4.3 Keyestudio Uno R3

The Keyestudio Uno R3 is a microcontroller board

based on the ATmega328, offering complete

compatibility with the Arduino Uno R3, which is

shown in Figure 2. It features a comprehensive set

of hardware components, including 14 digital

input/output pins (with 6 capable of functioning as

PWM outputs), 6 analog inputs, a 16 MHz quartz

crystal, a power jack, USB connectivity, and a reset

button, which are listed in Table 1.

Fig. 2: Keyestudio Uno R3

Given its full compatibility with the Arduino

Uno R3, the Keyestudio Uno R3 can be effortlessly

programmed using the Arduino IDE. The

microcontroller helps in controlling the water level

of the proposed system. It transfers the sensed water

level to the IoT platform and also to the smartphone.

The former can verify the water level and the pipe

will be controlled based on the water level.

4.4 LoRaWAN Shield

The LoRa shield is a wireless transceiver module

designed for long-range communication with

Arduino microcontroller boards, which is shown in

Figure 3. This shield enables the transmission of

data over extended distances while maintaining low

data rates. It harnesses spread spectrum

communication technology, known for its

remarkable interference resistance and energy

efficiency.

Table 1. KEYESTUDIO UNO R3 Specifications

S. No

Parameter

Specifications

Controller

ATmega 328P-PU

Input Voltage

7-12V

Digital

Input/Output Pins

14 (of which 6 provide

PWM output)

PWM Pins

6 (D3, D5, D6, D9, D10,

D11)

Analog Input Pins

6 (A0-A5)

DC Current per

I/O Pin

20 mA

Flash Memory

32 KB

SRAM

2 KB

EEPROM

1 KB

Clock Speed

16 MHz

LED_BUILTIN

D13

The LoRa shield is powered by the Semtech

SX1276/SX1278 chip, well-suited for wireless

sensor network applications like precision

agriculture, smart cities, and building automation. It

achieves exceptional sensitivity, surpassing -148

dBm, thanks to a cost-effective crystal and

materials. Furthermore, it incorporates a built-in +20

dBm power amplifier, significantly enhancing the

link budget for any application requiring extended

range or robust connectivity, which are listed in

Table 2. In comparison to conventional systems, the

LoRa shield offers notable advantages in terms of

blocking, selectivity, interference resilience, and

power efficiency.

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

Fig. 3: LoRaWAN Shield

Table 2. LoRaWAN Specifications

S. No

Parameter

Specifications

Max. Link

budget

168 dB

RF Output

+20 dBm at 100mW

efficiency

14dBm

Bit rate

300Kbps

Max.

Sensitivity

Down to -148dBm

RSSI

127

4.5 Analog Current to Voltage Converter

Typically, industrial sensors and devices generate a

current signal output ranging from 420 MA. 25 mA

current signal is converted into a 0~3V voltage

signal by using this analog converter, making it

effortless for the main control board to receive

input from the sensor.

In fault diagnosis, current signals lower than 4

mA are commonly employed, while signals

exceeding 20 mA are utilized for overrun detection.

Consequently, this converter is meticulously

engineered with a detection range spanning from

0~25 mA, catering to both fault detection and

overrun detection applications. This converter

incorporates a high-precision 0.1% sense resistor

and an ultra-low-noise rail-to-rail zero-drift

operational amplifier, ensuring remarkable

accuracy without the need for calibration, which is

shown in Figure 4. It offers the convenience of

operating on a wide voltage power supply, ranging

from 3.3V to 5.5V, and produces a 0 to 3V voltage

signal output. This compatibility extends to a wide

range of microcontroller boards and makes it

adaptable to a diverse array of applications. Its

specifications are listed in Table 3.

Fig. 4: Analog Current to Voltage Converter

Table 3. Analog Current to Voltage Converter

Specifications

S. No

Parameter

Specifications

Voltage

3.3-5.5 V

Detection Range

0-25 mA DC

Accuracy

±0.5% F.S. @ 16-bit

ADC, ±2% F.S. @ 10-

bit ADC

Termination

Resistance

120Ω

Connector

PH2.0-3P

4.6 Pressure-based Liquid Level Sensor

The sensor is a submersible liquid level sensor that

incorporates a high-performance pressure-sensing

chip, sophisticated processing circuitry, and

temperature compensation technology, which is

shown in Figure 5. This sensor is designed to detect

varying pressures at different liquid depths or levels,

converting these pressure signals into a current

output signal. The resulting current output signal,

ranging from 4 to 20mA, is subsequently

transformed into a voltage signal using an analog

current-to-voltage converter before being inputted

into the microcontroller board.

Crafted from stainless steel and fortified with

anti-corrosion materials, the sensor boasts an IP68

protection rating. It is versatile, and capable of

functioning effectively with a wide range of liquids,

including water, oil, and high-viscosity substances.

The sensor consistently delivers reliable

performance across diverse measurement scenarios,

spanning from rivers, reservoirs, and groundwater to

water tanks. The pressure-based liquid sensor

specifications are listed in Table 4.

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

Fig. 5: Pressure-based Liquid Level Sensor

Table 4. Pressure-based Liquid Sensor

Specifications

S. No

Parameter

Specifications

Cable Length

Measure Range

0-5m

Overall Accuracy

0.5%

Output Signal

4-20mA

Operating Voltage

12-36V

Operating Temperature

-20℃-70℃

Overload Capacity

300%

4.7 Measurement Principle

The liquid level sensor node was developed with the

primary purpose of monitoring fluctuations in the

water table. However, due to certain limitations,

such as the availability of water table observation

wells, the system underwent testing within a

controlled laboratory environment. To simulate the

absence of a water table observation well, a 2-meter-

long, 4-inch PVC pipe was utilized. This pipe was

filled with water to a specific height, and the sensor

was positioned at the pipe's base. A water release

valve was installed at the pipe's bottom to regulate

the controlled release and replenishment of water,

effectively simulating water level fluctuations.

The water level measurement setup is shown in

Figure 6. The sensor employed in this setup is a

hydrostatic pressure sensor capable of measuring the

pressure of stationary fluids. Hydrostatic pressure is

the result of the gravitational force acting on a static

liquid at a measurement point. Irrespective of the

shape or volume of the well or pipe, the hydrostatic

pressure at the measuring point within the pipe or

well remains directly proportional to the liquid's

height. The formula for calculating the liquid level is

as follows:

D = P2 / (ρ*g) (1)

P2: Pressure of the liquid upon the sensor

ρ: Liquid density

g: Local gravity acceleration

P1: Atmospheric pressure on liquid surface

D: Depth between the sensor and the liquid surface

Fig. 6: Measurement of Water Level in an Open and

Vented Pipe

In an open or vented pipe, there is a continuous

equalization of pressure between the surrounding air

and the gas phase above the liquid. The ambient

pressure, which exerts an additional "force" on the

medium, mirrors the ambient pressure affecting the

entire system, including the level sensor. When

using a relative pressure sensor that is already

compensated for ambient pressure, it inherently

corrects for the influence of this ambient pressure on

the level measurement. In essence, a relative

pressure sensor in a vented pipe effectively nullifies

the ambient pressure above the liquid, allowing the

hydrostatic pressure to solely represent the liquid's

depth. Once the liquid pressure has been captured

by the liquid level sensor, the signal is then

amplified and adjusted by the circuit, and

subsequently output as a standard 4 to 20 mA

analog current signal. The relationship between the

output current of the liquid level transmitter, the

output voltage of the current-to-voltage module, and

the depth is depicted below in Figure 7. It shows the

relationship between the analog current ranging

from 4 to 20 mA and voltage. The analog current

will be converted to voltage signals ranging from

0.48 V to 2.4 V which represent liquid depth of 0 to

5 meters.

Fig. 7: Relationship of Output Current to Voltage

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

4.8 Blynk IoT Platform

Following the collection of liquid-level data by the

sensor, the information is transmitted to an IoT

platform known as Blynk. Blynk provides a

comprehensive suite of software tools suitable for

prototyping, deploying, and remotely managing

connected devices, whether for personal IoT

projects or scaling up to serve millions of

commercially connected products.

Users can seamlessly connect their IoT devices

to the cloud and construct iOS, Android, and web

applications without the need for coding. These

applications enable real-time or historical data

analysis from the information sent by IoT devices.

The platform additionally empowers users to

remotely control their devices from anywhere across

the globe and receive critical notifications, among

other functionalities. Blynk streamlines the

management of multiple devices performing

identical functions through the use of Device

Templates. Essentially, a Device Template

comprises a collection of configurations. Once a

template is created, IoT devices can be generated

from it, inheriting all associated configurations. A

crucial component within the template is the

template ID, serving as a unique identifier for each

template, which must be incorporated into the code

on the IoT device.

Blynk can accept both raw and processed data

originating from any sensor or actuator linked to the

MCU board. A data stream serves as a conduit that

informs Blynk about the nature of the data it carries.

When data is transmitted to Blynk, it traverses a

data stream using the Blynk protocol. Each

incoming data point is automatically timestamped

and archived within the Blynk Cloud database.

Virtual Pins play a pivotal role in Blynk,

facilitating the exchange of data between IoT

devices and the Blynk platform. These pins enable

data to be dispatched from the Blynk App,

processed on the microcontroller, and subsequently

relayed back to the smartphone. Blynk can be

harnessed to trigger functions, access I2C devices,

perform value conversions, control servos, DC

motors, and much more. Virtual pins also offer the

flexibility to implement customized interface

functionality and interface with external libraries

like Servo, LCD, and others. When it comes to

sending and storing data, it can be preserved in its

original form or averaged into one-minute intervals.

Averaging entails consolidating multiple values sent

within a minute into a single value stored by Blynk.

However, real-time data remains visible on the

dashboard.

Blynk excels at visualizing data, enabling users

to present it in the form of charts or graphs.

Dashboards are constructed using building blocks

referred to as Widgets. These user interface layouts

are an integral component of the Device Template,

ensuring that when the template's layout is updated,

the user interface across all devices is also

automatically updated. Blynk has some standard

Widgets which are ready to use. Users can build

their own dashboard based on their preferences.

Besides, some function buttons can be created on

the dashboard for example buttons to set the data

collection time interval.

5 Results

In this section, IoT devices like Uno R3

microcontroller, LoRaWAN gateway, pressure-

based liquid level sensor, and Blynk IoT platform

are used to monitor the water level in an agriculture

sector. The pressure sensor detects the water level

and sends the same to the microcontroller. The

microcontroller verifies the received sensed data

with the threshold value. If the received data is

greater than the threshold, then the valve will be in

an off position and the pipe will not spill the water.

If it is less than the threshold, then the valve will be

on and the pipe will spill the water.

As previously mentioned, a 2-meter PVC pipe

serves as a simulation for water table observation.

The pipe is filled with water, reaching a height of

approximately 1.7 meters. Subsequently, the sensor

is positioned at the base of the pipe to gauge the

pressure at its lowest point. The pressure

measurements are then converted into liquid height,

as elaborated in the previous section.

From the setup, the water level is measured and

is transferred to the Blynk, an IoT platform. Figure

8 shows the data visualized in a line chart from

Blynk using a web browser. The sensor measures

the liquid level every 30 minutes for a total duration

of 22 hours. The pipe has been fixed with a valve to

release water with an appropriate flow rate. The

reason for this release valve is to simulate the water

level fluctuation to verify the sensor can detect and

measure the changes in the water level.

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

Fig. 8: Water Level Line Chart from Web Browser

Referring to Figure 8, the sensor measured the

highest level which is at 1.72 meters. The water

level was measured every 30 mins, the water level

showed a reduction over time until approximately

0.8 meters in height.

Fig. 9: Water Level and Battery Life Information

from Smartphone Apps

The sensed data is stored in the web browser

and the same is forwarded to the smart devices.

Apart from viewing the data from a web browser.

Blynk also provides Apps for the user to view the

data from a smartphone. Figure 9 shows the water

level and battery life from the IoT node. Users can

configure the update interval by using the customize

update button in the Apps. The update interval is

configured as 1802 seconds which is approximately

30 mins. The data also can be plotted in Blynk

Apps. Figure 10 shows the water level line chart in

auto-scale

Fig. 10: Water Level Data Line Chart in Blynk Apps

Continuous monitoring of sensed data i.e., water

level monitoring and battery monitoring is stored in

the web browser and also forwarded to the

smartphone, which helps the farmer to monitor the

agriculture fields and to enhance the crop yields.

6 Discussions

In this paper, a water monitoring system is designed

with a low power wide area network and IoT

devices. The major component is a pressure-based

liquid level sensor. It detects the water level and

forwards the same to the LoRaWAN gateway. With

the internet, the sensed data is forwarded to the

Blynk IoT platform. The water level is measured for

every half an hour and the reduction rate of water is

identified. As the time increases the water level

reduces which is shown in Figure 9. As the water

level reduces below the precision level then the

PVC pipe will spill the water by opening the valve.

Along with the water level, the proposed

network detects the battery level. It will help in

continuous detection of the water level without

system shutdown. This continuous detection and

sharing of sensed data through smartphones helps

the farmers to verify the crop growth and yield

remotely. Agriculture precision monitoring systems

help farmers to enhance their crop yield.

7 Conclusions

In this paper, the development and use of

LoRaWAN-based IoT systems for monitoring water

level fluctuation is considered. Initially, the

prototype for measuring the water level has been

designed, then coded, and finally tested

successfully. The pressure-based liquid level sensor

in the prototype measured the data and the same

data was forwarded to the Blynk IoT platform by

using LoRaWAN wireless communication

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

technology. Besides, the IoT sensor node is capable

of monitoring the battery status so that the battery

can be charged or replaced before it runs flat to

avoid any downtime. The battery life and water

level data were able to be visualized in the Blynk

IoT platform. The water level has been plotted in a

line chart and the data is accessible either using a

web browser from a PC or Blynk Apps via a

smartphone.

Future work in the area of LoRaWAN-based

IoT systems includes code optimization to maximize

the battery life. Besides, the node can be added with

more sensors that are capable of collecting different

types of environmental data such as moisture

sensors, rain sensors, etc., which will help to

optimize the cost.

Acknowledgement:

This work was supported and funded by Leave a

Nest Grant, MMUE/220070.

References:

[1] United Nations, Department of Economic and

Social Affairs, Population Division

(2022). World Population Prospects 2022,

Online Edition, [Online].

https://population.un.org/wpp/ (Accessed

Date: April 19, 2024).

[2] Nižetić, Sandro, Petar Šolić, Diego Lopez-de-

Ipiña Gonzalez-De, and Luigi Patrono.

"Internet of Things (IoT): Opportunities,

issues and challenges towards a smart and

sustainable future." Journal of cleaner

production, 274 (2020): 122877. doi:

10.1016/j.jclepro.2020.122877.

[3] Elijah, Olakunle, Tharek Abdul Rahman,

Igbafe Orikumhi, Chee Yen Leow, and MHD

Nour Hindia. "An overview of Internet of

Things (IoT) and data analytics in agriculture:

Benefits and challenges." IEEE Internet of

things Journal, 5, no. 5 (2018): 3758-3773.

[4] Hamadani, H., S. Mudasir Rashid, J. D.

Parrah, A. A. Khan, K. A. Dar, A. A. Ganie,

A. Gazal, R. A. Dar, and Aarif Ali.

"Traditional farming practices and its

consequences." Microbiota and Biofertilizers,

Vol 2: Ecofriendly Tools for Reclamation of

Degraded Soil Environs (2021): 119-128.

Springer, Cham, https://doi.org/10.1007/978-

3-030-61010-4_6.

[5] Patel, Sanoj Kumar, Anil Sharma, and Gopal

Shankar Singh. "Traditional agricultural

practices in India: an approach for

environmental sustainability and food

security." Energy, Ecology and

Environment, 5, no. 4 (2020): 253-271,

https://doi.org/10.1007/s40974-020-00158-2.

[6] Sharma, Bhuvika, Upanshu Aggarwal,

Komalpreet Kaur, Hitika Saini, Sujata Rani,

Parteek Kumar, Karun Verma, and Yosi

Shacham. "Role of Remote Sensing in

Precision Agriculture." In Proceedings of 3rd

International Conference on Recent Trends in

Machine Learning, IoT, Smart Cities and

Applications: ICMISC 2022, pp. 619-630.

Singapore: Springer Nature Singapore, 2023,

https://doi.org/10.1007/978-981-19-6088-

8_57.

[7] Akhtar, Mohammad Nishat, Abdurrahman

Javid Shaikh, Ambareen Khan, Habib Awais,

Elmi Abu Bakar, and Abdul Rahim Othman.

"Smart sensing with edge computing in

precision agriculture for soil assessment and

heavy metal monitoring: A

review." Agriculture 11, no. 6 (2021): 475,

https://doi.org/10.3390/agriculture11060475.

[8] Akhter, Ravesa, and Shabir Ahmad Sofi.

"Precision agriculture using IoT data analytics

and machine learning." Journal of King Saud

University-Computer and Information

Sciences 34, no. 8 (2022): 5602-5618,

https://doi.org/10.1016/j.jksuci.2021.05.013.

[9] Roslee, Mardeni, Khazaimatol Shima Subari,

and Intan Suraya Shahdan. "Design of bow tie

antenna in CST studio suite below 2GHz for

ground penetrating radar applications."

In 2011 IEEE International RF & Microwave

Conference, Seremban, Malaysia, pp. 430-

433. IEEE, 2011.

[10] Sudhamani, Chilakala, Mardeni Roslee, Jun

Jiat Tiang, and Aziz Ur Rehman. "A survey

on 5G coverage improvement techniques:

Issues and future challenges." Sensors 23, no.

4 (2023): 2356.

[11] Krishnamoorthy, U., Gandhi Ayyavu, P.,

Panchal, H., Shanmugam, D., Balasubramani,

S., Al-rubaie, A.J., Al-khaykan, A., Oza,

A.D., Hembrom, S., Patel, T. and Vizureanu,

P. Efficient Battery Models for Performance

Studies-Lithium Ion and Nickel Metal

Hydride Battery. Batteries, 9(1), (2010) p.52,

https://doi.org/10.3390/batteries9010052.

[12] Singh, Ajay, Peter Krause, Sudhindra N.

Panda, and Wolfgang-Albert Flugel. "Rising

water table: A threat to sustainable agriculture

in an irrigated semi-arid region of Haryana,

India." Agricultural Water Management 97,

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

no. 10 (2010): 1443-1451,

https://doi.org/10.1016/j.agwat.2010.04.010.

[13] Hibbs, Barry J. "Commentary and review of

modern environmental problems linked to

historic flow capacity in arid groundwater

basins." Geosciences 12, no. 3 (2022): 124,

https://doi.org/10.3390/geosciences12030124.

[14] Matlala, Malebo. ‘Groundwater Dynamics in

Transboundary Aquifers of Southern Africa’.

Environmental Sciences, IntechOpen, 10 Feb.

2023. Crossref, doi:

10.5772/intechopen.109906.

[15] Ramirez, Saul G., Gustavious Paul Williams,

Norman L. Jones, Daniel P. Ames, and Jani

Radebaugh. "Improving Groundwater

Imputation through Iterative Refinement

Using Spatial and Temporal Correlations from

In Situ Data with Machine

Learning." Water 15, no. 6 (2023): 1236.

[16] Roy, Sunanda, Jun-Jiat Tiang, Mardeni Bin

Roslee, Md Tanvir Ahmed, Abbas Z.

Kouzani, and MA Parvez Mahmud. "Design

of a highly efficient wideband multi-

frequency ambient RF energy

harvester." Sensors 22, no. 2 (2022): 424.

[17] Bhunia, Gouri Sankar, and Uday Chatterjee.

"Ground water depletion and climate change:

role of geospatial technology for a mitigation

strategy." In Climate Change, Community

Response and Resilience, pp. 291-304.

Elsevier, 2023, doi: 10.1016/B978-0-443-

18707-0.00015-1.

[18] Hubert, Bernard, Mark Rosegrant, Martinus

AJS Van Boekel, and Rodomiro Ortiz. "The

future of food: scenarios for 2050." Crop

Science 50 (2010): S-33.

[19] Sharma, Neha, Madhavi Shamkuwar, and

Inderjit Singh. "The history, present and

future with IoT." Internet of things and big

data analytics for smart generation (2019):

27-51. Springer.

[20] Xiaohui Wang and Nannan Liu, “The

application of internet of things in agricultural

means of production supply chain

management”, Journal of Chemical and

Pharmaceutical Research, 2014, 6(7):2304-

2310, ISSN: 0975-7384, 2014.

[21] Jain, Deeksha, P. Venkata Krishna, and

Vankadara Saritha. "A study on Internet of

Things based applications." arXiv preprint

arXiv:1206.3891 (2012). .

[22] Ali, Terteil AA, Viraj Choksi, and Madhukar

B. Potdar. "Precision agriculture monitoring

system using green internet of things (g-iot)."

In 2018 2nd International Conference on

Trends in Electronics and Informatics

(ICOEI), Tirunelveli, India, pp. 481-487.

IEEE, 2018.

[23] Divakar K M, Archana, Sushma K R, Iot

Technology in Smart Farming, International

Research Journal of Engineering and

Technology (IRJET), e-ISSN: 2395-0056

Volume: 05 Issue: 05, May-2018.

[24] Farzad Kiani, Amir Seyyedabbasi, Wireless

Sensor Network and Internet of Things in

Precision Agriculture, International Journal

of Advanced Computer Science and

Applications, Vol. 9, No. 6, 2018.

[25] Sushmita Tapashetti, Dr. Shobha K.R.,

Precision Agriculture using LoRa,

International Journal of Scientific &

Engineering Research, Vol. 9, Issue 5,

pp.2023-2028, 2018.

[26] Huma Zia, Ahsan Rehman, Nick R. Harris,

Sundus Fatima and Muhammad Khurram, An

Experimental Comparison of IoT-Based and

Traditional Irrigation Scheduling on a Flood-

Irrigated Subtropical Lemon Farm, Sensors,

21, 4175, 2021.

[27] M Gayathri, D Arun Shunmugam, Ishwariya,

Smart Irrigation System using IoT, JIRMPS,

Volume 9, Issue 3, ISSN: 2349-7300 2021

[28] Davcev, Danco, Kosta Mitreski, Stefan

Trajkovic, Viktor Nikolovski, and Nikola

Koteli. "IoT agriculture system based on

LoRaWAN." In 2018 14th IEEE International

Workshop on Factory Communication

Systems (WFCS), Imperia, Italy, pp. 1-4.

IEEE, 2018.

[29] Vadapalli, Adithya, Swapna Peravali, and

Venkatarao Dadi. "Smart agriculture system

using IoT technology." Publisher:

International Journal of Advance Research in

Science and Engineering (2319-8354) 9

(2020): 58-65.

[30] Boonyopakorn, Pongsarun, and Thanamate

Thongna. "Environment monitoring system

through LoRaWAN for smart agriculture."

In 2020-5th International Conference on

Information Technology (InCIT), pp. 12-16.

IEEE, Chonburi, Thailand, 2020.

[31] Fidantemiz, Yavuz F., Xinhua Jia, Aaron LM

Daigh, Harlene Hatterman-Valenti, Dean D.

Steele, Ali R. Niaghi, and Halis Simsek.

"Effect of water table depth on soybean water

use, growth, and yield parameters." Water 11,

no. 5 (2019): 931.

[32] Ramson, S.J., León-Salas, W.D., Brecheisen,

Z., Foster, E.J., Johnston, C.T., Schulze, D.G.,

Filley, T., Rahimi, R., Soto, M.J.C.V.,

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024

Bolivar, J.A.L. and Malaga, M.P. A self-

powered, real-time, LoRaWAN IoT-based soil

health monitoring system. IEEE Internet of

Things Journal, 8(11), (2021), pp.9278-9293.

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This work was funded by Leave a Nest Grant,

MMUE/220070.

Conflict of Interest

The authors have no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2024.15.5

Mardeni Roslee, Tim Yap Woon,

Chilakala Sudhamani, Indrarini Dyah Irawati,

Denny Darlis, Anwar Faizd Osma, Mohamad Huzaimy Jusoh

E-ISSN: 2415-1513

Volume 15, 2024