Detection and Prevention of Termite Infestation Using IoT through

Machine Learning

Soumya Madduru, Bindu Devarakonda, Bhumika Bhumireddy,

Bhanuprakash Boya and Purushotham Dandu

Department of CSE, Srinivasa Ramanujan Institute of Technology, Rotarypuram Village, B K Samudram Mandal,

Anantapuramu - 515701, Andhra Pradesh, India

Keywords: LCD, Alert, Raspberry PI, Decision Making, GSM.

Abstract: This project outlines an innovative system designed for the detection and prevention of termite infestations in

furniture and walls, utilizing IoT and machine learning technologies. At the core of the system is a Raspberry

Pi microcontroller, which manages and processes data from various environmental sensors. The system

includes a DHT11 sensor to monitor temperature and humidity, and a soil moisture sensor to detect moisture

levels both of which are critical indicators of termite activity. Additionally, an ADC module is incorporated

to ensure accurate data acquisition from analog sensors, enhancing the reliability of the information being

processed. The data gathered by the sensors is analyzed using a Random Forest machine learning algorithm,

which identifies patterns that may suggest the presence of termites. When abnormal environmental conditions

are detected, the system triggers alerts through a GSM module, sending notifications to users, while a buzzer

provides a local warning. Real-time data is also displayed on an LCD, offering immediate access to critical

information. By combining IoT connectivity with advanced machine learning, this system enables proactive

monitoring and prevention of termite infestations, ultimately helping to protect furniture and structures from

damage and minimizing the risk of costly repairs.

1 INTRODUCTION

Termite infestations are a significant concern for

homeowners and businesses, causing severe damage

to furniture, walls, and other wooden structures.

Traditional methods of detecting and preventing

termite activity often rely on visual inspections,

which can be time-consuming and ineffective until

significant damage has already occurred. As a result,

there is a growing need for more efficient, proactive

solutions to monitor and address termite infestations

in real time. This project aims to address this need by

integrating advanced technologies such as the

Internet of Things (IoT) and machine learning to

create an intelligent system for the early detection and

prevention of termite damage.

The system leverages a Raspberry Pi

microcontroller at its core, which communicates with

multiple sensors to monitor environmental factors

that promote termite activity. By utilizing a

combination of temperature, humidity, and moisture

sensors, along with data analysis powered by a

Random Forest machine learning algorithm, the

system can detect patterns indicative of an infestation.

This innovative approach ensures early intervention

through automated alerts and real-time monitoring,

providing a reliable and efficient way to safeguard

valuable assets from termite damage. By combining

IoT with machine learning, this project offers a

cutting-edge solution that enhances the long-term

protection of furniture and walls.

In addition to its core functionality of detecting

termite infestations, the system also emphasizes ease

of use and prompt response to abnormal conditions.

The integration of a GSM module allows for

immediate notifications to be sent to users, ensuring

they are alerted as soon as the system detects

environmental changes that suggest potential termite

activity. A buzzer further enhances the alert system

by providing a local sound warning, which is

particularly useful in areas where immediate attention

is needed. With real-time information displayed on an

LCD screen, users can continuously monitor the

conditions and take necessary action without delay.

This multi-faceted approach not only enhances the

Madduru, S., Devarakonda, B., Bhumireddy, B., Boya, B. and Dandu, P.

Detection and Prevention of Termite Infestation Using IoT through Machine Learning.

DOI: 10.5220/0013924400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st Inter national Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

161-167

ISBN: 978-989-758-777-1

161

efficiency of termite detection but also empowers

users to act swiftly, preventing extensive damage and

reducing maintenance costs over time.

2 RELATED WORKS

The "Termite Detection Scanner: Design and

Development" by F. R. Mohammed, S. Prakash, D.

Brindha, and T. Kumaran, discusses a method to

identify the presence of termites that could potentially

cause severe damage to wooden structures and

buildings if not addressed promptly. Termites, which

thrive in colonies and feed on cellulose-rich

materials, can compromise the structural integrity of

a building. Detecting them early is crucial, and one

effective way to do so is through thermal imaging

technology. Thermal sensors are employed to monitor

temperature fluctuations within a given area or

system, enabling the detection of these pests by

observing temperature variations. The resulting data

is then presented on a screen as a thermal image,

allowing easy visualization of potential termite

presence.

The study titled *"Classroom Furniture

Vulnerability to Drywood Termite Infestation"* by

A. J. Mark Rojo, investigates the extent of drywood

termite infestations in classroom furniture at the

University of the Philippines Los Baños, College of

Forestry and Natural Resources, located in Laguna,

Philippines. Conducted in February 2016, the

research utilized nonparametric statistical tests to

assess whether the type of furniture, material

composition, and protective coatings could influence

susceptibility to termite damage. Out of all the

furniture examined, only 15% showed signs of

drywood termite infestation, including visible

damage and fecal pellets. The Kruskal-Wallis test

revealed a statistically significant variation in damage

ratings across different furniture types and materials

at a 95% confidence level. Furthermore, the Mann-

Whitney U test indicated that unpainted furniture was

significantly more prone to termite infestation. The

study also identified specific features such as cracks,

natural checks, misaligned or overlapping wood, and

exposed end grain as key factors that contribute to the

vulnerability of furniture, as they provide entry points

for termite swarmers (alates).

3 PROPOSED METHOD

The proposed method for termite detection and

prevention integrates Internet of Things (IoT) and

machine learning technologies to create an advanced

monitoring system. Central to this system is a

Raspberry Pi microcontroller that serves as the

processing unit, gathering data from various sensors.

These sensors include a DHT11 sensor for measuring

temperature and humidity, and a soil moisture sensor

to track moisture levels, both of which are key

indicators of termite activity. To ensure the accuracy

and reliability of the data, an Analog-to-Digital

Converter (ADC) module is used for precise data

acquisition from analog sensors. This setup allows

the system to continuously monitor environmental

factors that could signal potential termite

infestations.

Once the data is collected, it is analyzed using a

Random Forest machine learning algorithm, which is

capable of detecting patterns that indicate the

presence of termites. If the system identifies

abnormal environmental conditions, it triggers alerts

through a GSM module to notify users, while a local

buzzer provides an immediate warning. Additionally,

real-time data is displayed on an LCD screen, giving

users direct access to critical information. By

combining real-time sensor data with machine

learning capabilities, the system not only detects but

also helps prevent termite infestations, offering a

proactive solution to protect furniture and structures

from significant damage.

The system leverages a combination of real-time

environmental monitoring and advanced data

analysis to provide an efficient solution for termite

prevention. By continuously tracking temperature,

humidity, and moisture levels in the surrounding

environment, the system can detect fluctuations that

may indicate favorable conditions for termite

activity. The integration of machine learning further

enhances the system's ability to recognize patterns in

the data and make accurate predictions about

potential infestations. The use of IoT technology

ensures seamless communication between sensors,

the Raspberry Pi microcontroller, and the GSM

module, allowing for immediate notifications and

alerts. With this proactive approach, the system not

only alerts users to possible termite threats but also

enables timely intervention, reducing the risk of

extensive damage and the need for costly repairs.

3.1 Block Diagram

The figure 1 shows Block Diagram.

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Figure 1: Block Diagram.

4 METHODOLOGY

Hardware Required for this project

4.1 Raspberry Pi

Figure 2: Raspberry Pi.

In this project, the Raspberry Pi (figure 2) plays a

pivotal role as the central microcontroller that

manages and processes data from various

environmental sensors. Chosen for its versatility and

computing power, the Raspberry Pi acts as the

system's brain, collecting real-time data from sensors

like the DHT11 and soil moisture sensors. With its

GPIO (General Purpose Input/Output) pins, the

Raspberry Pi interfaces directly with the sensors,

converting analog readings into digital data that can

be processed and analyzed. The compact size and

processing capabilities of the Raspberry Pi make it an

ideal choice for IoT applications, enabling the

integration of multiple sensors and peripherals into a

single cohesive system.

Moreover, the Raspberry Pi facilitates seamless

connectivity for communication and alerting

functions within the system. Through its onboard Wi-

Fi or Ethernet capabilities, it can send notifications

via the GSM module to inform users of potential

termite activity. The microcontroller also displays

real-time data on an LCD screen, providing

immediate access to critical environmental

information. By using the Raspberry Pi, the project

can harness the power of machine learning algorithms

to analyze the sensor data, improving accuracy in

detecting and predicting termite infestations. This

makes the Raspberry Pi a key component in achieving

the project's goal of proactive termite detection and

prevention.

4.2 MOISTURE SENSOR

The soil moisture sensor (figure 3) in this project

plays a critical role in detecting environmental

conditions that may contribute to termite activity.

Termites are highly sensitive to moisture levels, and

they often thrive in areas with high humidity or excess

moisture, such as walls or furniture exposed to damp

conditions. By monitoring the moisture content in the

surrounding environment, the sensor provides

valuable data that helps identify areas at risk of

termite infestations. The sensor works by measuring

the electrical resistance or capacitance in the soil,

with higher resistance indicating drier conditions and

lower resistance signaling increased moisture. This

allows the system to detect subtle changes in moisture

levels, which can be an early indicator of termite

presence or activity.

In the context of the termite detection system, the

soil moisture sensor enhances the overall accuracy of

the monitoring process by providing real-time data

that correlates with other environmental factors, such

as temperature and humidity. By integrating this

sensor with the machine learning algorithm, the

system can better understand patterns and trends in

the environment, leading to more accurate predictions

of potential termite infestations. The sensor's ability

to detect even small fluctuations in moisture levels

ensures that the system can react quickly to changes

in the environment, alerting users to possible termite

threats before they cause significant damage. This

proactive monitoring approach helps protect both

furniture and structures, reducing the need for

expensive repairs.

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Figure 3: Moisture Sensor.

4.3 GSM

In this project, the GSM module plays a crucial role

in enabling remote communication and alert

functionality. The GSM module is integrated with the

Raspberry Pi microcontroller, allowing the system to

send notifications directly to the user's mobile phone

when abnormal conditions are detected. This feature

is particularly important for users who may not be in

close proximity to the monitored area, as it ensures

they are promptly informed of any potential termite

infestations. Once the sensors detect environmental

anomalies such as unusual temperature, humidity, or

moisture levels, the GSM module is triggered to send

a text message alert to the user, providing them with

real-time information about the status of the system.

Figure 4: GSM.

The GSM (figure 4) module enhances the overall

functionality of the system by offering a reliable

communication channel, independent of local

network infrastructure. This means that even if the

user is outside of the immediate vicinity or in an area

without internet access, they will still receive critical

alerts via SMS. The ability to send alerts through

SMS ensures that users can take immediate action to

address the problem, whether by inspecting the area

further or contacting pest control services. In

combination with the other components of the

system, the GSM module ensures that termite

detection and prevention efforts are timely and

effective, ultimately helping to safeguard valuable

furniture and structures from damage.

4.4 LCD

In this project, an LCD screen is incorporated to

provide real-time feedback and display vital

information related to the environmental conditions

monitored by the sensors. The LCD offers a clear and

intuitive interface, allowing users to quickly access

data on temperature, humidity, and soil moisture

levels, which are key indicators of termite activity. By

continuously updating the display, the LCD ensures

that the user has up-to-date insights into the

conditions within the monitored space, making it

easier to track potential termite risks. This real-time

display also serves as an immediate visual reference

for the system’s status, offering a user-friendly

method to monitor the system's performance and

detect abnormalities.

Figure 5: LCD.

The use of the LCD (figure 5) enhances the

system’s functionality by presenting the data in a

structured and readable format. Instead of relying

solely on alerts or notifications, users can visually

assess the environmental conditions at a glance,

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making it easier to interpret sensor data. Whether the

system is running normally or triggering warnings,

the LCD provides an accessible overview of all

relevant parameters. This contributes to the overall

effectiveness of the system, ensuring that users have

the necessary information to make informed

decisions regarding termite prevention and

maintenance, ultimately helping protect their

furniture and structures from potential damage.

4.5 Buzzer

In this project, the buzzer plays a critical role in

providing immediate, local alerts when the system

detects potential termite activity. The buzzer is

connected to the Raspberry Pi microcontroller and is

triggered whenever abnormal environmental

conditions, such as a rise in temperature or moisture

levels, are detected by the sensors. This serves as an

audible warning, notifying users in the vicinity of

potential termite presence, enabling quick action to

address the issue before it escalates into significant

damage. The buzzer's sound ensures that the alert is

heard even in noisy environments, making it a reliable

tool for raising awareness about the problem.

Figure 6: Buzzer.

The buzzer also complements the GSM module,

which sends remote notifications to users. While the

GSM module communicates the alert over long

distances, the buzzer provides an immediate response

to any changes in the local environment. This dual-

alert system increases the effectiveness of the

monitoring system by ensuring that users are alerted

both locally and remotely. The use of a buzzer adds a

layer of responsiveness and efficiency to the system,

contributing to the overall goal of proactive termite

infestation prevention. The figure 6 shows buzzer.

4.6 Power supply

The power supply board in this project is designed to

efficiently convert and regulate input power to meet

the requirements of various system components. It

accepts an input voltage range from 12V to 18V and

utilizes two separate regulators: a 12V regulator and

a 5V regulator. The 12V regulator provides a stable

12V output to power components that require this

voltage, such as the Raspberry Pi and certain sensors.

Meanwhile, the 5V regulator ensures that components

requiring 5V, such as the GSM module and certain

other sensors, receive the appropriate power supply.

By splitting the output into two regulated voltages,

the power supply board ensures that all components

receive the correct voltage, preventing damage and

ensuring the system operates reliably.

The board is equipped with essential components

such as capacitors and diodes, which work together to

provide smooth and efficient power conversion. A

bridge rectifier, made up of diodes, is included to

convert the alternating current (AC) input into direct

current (DC), ensuring that the power supply provides

the necessary DC output for the system's components.

Capacitors are placed across the output lines to filter

out any voltage spikes or fluctuations, providing

stable and clean power. This well-designed power

supply board is crucial for the proper functioning of

the system, ensuring all components are powered

reliably while protecting them from voltage

irregularities.

Advantages and Applications

Advantages

 Proactive

 Efficient

 Reliable

 Cost-effective

 Smart

 Scalable

 Real-time

 Sustainable

 User-friendly

Applications

 Pest control

 Home automation

 Building maintenance

 Environmental monitoring

 Smart homes

 Agriculture

Detection and Prevention of Termite Infestation Using IoT through Machine Learning

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 Structural health

 IoT security

 Property protection

 Forestry management

5 RESULTS

Figure 7: Implementation Code.

Figure 8: User Details.

The figure 7 shows Implementation Code and

figure 8 shows Figure 8: User Details.

Figure 9: Testing Wooden pieces.

Figure 10: Sending Alerts to User.

The figure 9 shows Testing Wooden pieces and

figure 10 shows Sending Alerts to User.

Figure 11: Graphical Updates on Thing Speak.

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Figure 12: Circuit Diagram -Total KIT.

The figure 11 shows Graphical Updates on Thing

Speak and figure 12 shows Circuit Diagram -Total

KIT.

6 CONCLUSIONS

In conclusion, the proposed termite detection and

prevention system successfully integrates IoT and

machine learning technologies to provide an efficient

and proactive solution for monitoring and managing

termite infestations. By utilizing sensors to measure

critical environmental factors such as temperature,

humidity, and moisture, the system can detect

fluctuations that indicate the presence of termites.

The combination of real-time data analysis through

machine learning and seamless communication via

IoT ensures that the system can accurately identify

potential threats, triggering alerts through both GSM

notifications and local buzzers. This integrated

approach not only helps prevent significant structural

damage but also empowers users to take timely

action, ultimately reducing the risk of costly repairs

and enhancing the protection of valuable furniture

and buildings.

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