Design and Development of a Weather Drone Using IoT

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 1635
Design and Development of a Weather Drone Using IoT
Gooneelkumar Bannenavar1, Aman Aslam Hudli2, Mohammed Shahid Mulla3, Prajwal Nayak4 ,
Suprit Malagi3 , Dr. Rajendra M Galagali 6
1,2,3,4Students, Department of Mechanical Engineering S.G. Balekundri Institute of Technology Engineering school
in Belgaum, Karnataka,India
5Professor and Project guide, Department of Mechanical Engineering, S.G. Balekundri Institute of Technology,
Belagavi, Karnataka, India
6 Professor and HOD, Department of Mechanical Engineering, S.G. Balekundri Institute of Technology, Belagavi,
Karnataka, India
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Abstract - The use of drones in various fields is growing
rapidly, and weather forecasting is no exception. In this
paper, we present the design and development of a weather
drone using IoT technology. The drone is equipped with
sensors to collect weather data like temperature and
himidity. The data is then transmitted to a cloud server via a
wireless connection, where it can be analyzed and used to
provide accurate weather forecasts.
Key Words: weather, temperature, humidity, IOT etc
1.INTRODUCTION
The use of weather forecasting is vital in our day-to-day
life. It is used in various industries like aviation,
transportation, agriculture, and many more. Traditional
methods of weather forecasting involve ground-based
sensors, weather balloons, and satellites. However, these
methods can be expensive and time-consuming. Drones
have emerged as a viable alternative to traditional
weather forecasting methods due to their ability to collect
data from inaccessible locations quickly.
The use of IoT technology in drones has enabled the
collection of real-time data and transmission to cloud
servers for analysis. In this paper, we present the design
and development of a weather drone using IoT technology.
1.1 LITERATURE SURVEY
One critical aspect of weather drone design is the
integration of IoT sensors for data collection. Researchers
(Smith et al., 2018; Zhang et al., 2020) have explored the
integration of sensors such as temperature, humidity, wind
speed, air pressure, and rainfall sensors onto drones,
enabling real-time data collection at different altitudes and
locations. These studies emphasize the importance of
selecting accurate and reliable sensors and ensuring
proper calibration for precise weather data measurements.
Efficient data transmission is crucial for timely weather
data analysis and decision-making. Several studies (Li et al.,
2017; Rizvi et al., 2019) have proposed different
communication systems for weather drone applications.
These include wireless technologies such as Wi-Fi, cellular
networks, and satellite communication. The research
highlights the need for reliable and secure data
transmission protocols to ensure seamless connectivity
between the drone and the IoT platform.
Analyzing and visualizing weather data collected by drones
is essential for meaningful interpretation and decision-
making. Researchers (Chen et al., 2018; Park et al., 2021)
have explored various data analysis techniques, including
machine learning algorithms and statistical methods, to
extract valuable insights from weather drone data.
Visualization techniques, such as charts, graphs, and maps,
have also been employed to present the data in a user-
friendly and informative manner.
The use of weather drones in agriculture has shown
significant potential for improving crop management and
yield. Researchers (Wang et al., 2019; Zhang et al., 2021)
have demonstrated the benefits of weather drone systems
in optimizing irrigation schedules, predicting crop water
requirements, and monitoring pest and disease outbreaks.
These studies emphasize the importance of integrating
weather drone data with agronomic models and decision
support systems to assist farmers in making
informed decisions.
1.2 METHODOLOGY
Problem Definition:
The research aims to address the problem of effective
weather monitoring in various fields, such as agriculture,
environmental monitoring, and disaster management. The
existing weather monitoring systems have limitations in
terms of data accuracy, real-time transmission, and
accessibility. The proposed solution is to develop a
weather drone using IoT technology, integrating hardware
components and data transmission mechanisms to collect,
analyze, and transmit weather data in real-time.

System Design:
The system design involves carefully selecting and
integrating hardware components to build the weather
drone. The NodeMCU is chosen for data transmission,
providing a reliable and efficient way to send weather data
wirelessly. The OpenPilot CC3D serves as the flight
controller circuit, enabling stable flight and precise
control. The DHT sensor is employed to sense humidity
and temperature accurately. The system design ensures
compatibility between the hardware components,
allowing seamless integration and optimal performance.
Hardware Implementation:
The weather drone hardware components are
assembled following a systematic approach. The NodeMCU
is connected to the flight controller circuit, facilitating
real-time data transmission from the drone. The DHT
sensor is carefully integrated, ensuring proper positioning
for accurate humidity and temperature sensing. The
hardware implementation involves soldering, mounting,
and securing the components on the drone frame to
ensure stability and durability.
Data Acquisition and Display:
The DHT sensor is utilized to acquire weather data,
specifically humidity and temperature. The sensor is
calibrated to provide accurate measurements. The
NodeMCU circuit is programmed to collect data from the
DHT sensor and send it to the on-board 16 x 2 LCD display.
The LCD display serves as a local interface, enabling
immediate visualization of the weather data on the drone
itself. The data acquisition and display system ensures
real-time access to weather information during drone
operations.
Real-time Data Transmission:
The data collected by the weather drone is transmitted
in real-time using the NodeMCU circuit. The NodeMCU is
configured to establish a connection with a cloud server,
enabling seamless data transmission. This connection is
secured using appropriate protocols and encryption
methods to protect the integrity and privacy of the
transmitted data. The real-time data transmission ensures
that weather data is promptly delivered to the intended
recipients for analysis and decision-making.
Android Application Development:
An Android application is developed to receive and
visualize the real-time weather data transmitted by the
weather drone. The application is designed to provide a
user-friendly interface for accessing weather information.
It fetches the data from the cloud server and displays it in
a clear and intuitive manner. The application allows users
to view current weather conditions, historical data, and
trend analysis, empowering them to make informed
decisions based on the collected weather data.
Website Development:
A website is created to complement the Android
application and to provide an alternative platform for
weather data visualization. The website is designed to be
responsive, allowing users to access it from various
devices. It connects to the cloud server to retrieve real-
time and historical weather data transmitted by the
weather drone.
Testing and Evaluation:
The developed weather drone system undergoes
rigorous testing and evaluation to ensure its functionality,
accuracy, and reliability. Various test scenarios are
executed to assess the performance of the hardware
components, data acquisition system, and data
transmission mechanisms. The collected weather data is
compared with established weather monitoring systems
for validation. Any issues or limitations identified during
the testing phase are addressed, and necessary
improvements are made to enhance the system's overall
performance.
2. STANDARD COMPONENTS USED
Fig1. Drone model
1. Frame or Chassis: Provides structural support and
holds all the components together.
2. Motors (1000 KV): Generates thrust for the
drone's flight.
3. Electronic Speed Controllers (ESC): Controls the
speed and direction of the motors.
4. Flight Controller: Processes sensor data, stabilizes
the drone, and controls its flight behavior.
5. NodeMCU: IoT platform for data transmission.

6. DHT Sensor: Measures humidity and temperature.
7. 16x2 LCD: Displays the collected weather data.
8. Propellers: Generate thrust and enable controlled
movement.
9. Battery: Provides power to the drone's
components.
10. Receiver and Transmitter: Facilitate
communication between the drone and the remote
control.
11. Cloud Server: Facilitates data transmission and
storage in the cloud.
12. Android Application: Receives and displays real-
time weather data from the drone.
a. Website: Provides a user interface to
access and visualize the collected weather
data.
13. Wi-Fi Module: Enables wireless connectivity for
data transmission.
14. On-board Microcontroller: Controls the
functioning of various components and processes
sensor data.
These components work together to enable the drone to
collect, process, and transmit weather data in real-time.
The drone's frame provides the physical structure, while
the motors, ESCs, flight controller, and sensors ensure
flight control, data collection, and stability. The NodeMCU,
cloud server, Android application, and website form the
data transmission and visualization infrastructure.
Collectively, these components make the weather drone a
powerful tool for monitoring and analyzing weather
conditions in various applications, including agriculture.
3. DESIGN CALCULATION
The weather drone was designed to collect weather data,
including temperature and humidity. The drone was
equipped with a GPS module to track its location and a
camera to capture images of the surrounding
environment. The drone was also equipped with an IoT
module to transmit data to a cloud server.
The sensors were connected to a microcontroller that was
programmed to read the data and transmit it to the IoT
module. The IoT module was connected to a wireless
module that transmitted the data to a cloud server. The
cloud server was used to store and analyze the data, which
was then used to provide accurate weather forecasts.
The drone was designed to be lightweight and compact to
enable easy transportation to remote locations. The
battery was designed to provide enough power for the
drone to fly for an extended period, allowing it to collect
data from multiple locations.
Lift & Thrust in Drone
Lift = 0.5 * ρ * V^2 * A * CL
where:
rho represents the air density.
V is the airspeed of the drone relative to the air.
A is the reference area
CL is the coefficient of lift
Thrust:
The thrust force generated by a propeller or rotor can be
calculated using the following formula:
Thrust = 0.5 * ρ * A * V^2 * CT
where:
ρ represents the air density.
A is the rotor disc area
V is the airspeed of the air entering the rotor or propeller.
CT is the coefficient of thrust
Thrust Calculation:
Thrust = 0.5 * ρ * A * V^2 * CT
Given that we have a 12" x 3.5" propeller, the rotor disc
area (A) can be calculated as:
A = π * (radius)^2
where the radius is half of the propeller diameter.
Lift Calculation:
Lift = 0.5 * ρ * V^2 * A * CL
let's assume the air density (ρ) to be approximately 1.225
kg/m^3, which is the standard value at sea level.
Motor: 1000kv
Battery: 11.1V, 2200mAh
Propeller: 12" x 3.5"
Battery capacity (Ah) = 2200mAh / 1000 = 2.2 Ah
Thrust = 0.5 * ρ * A * V^2 * CT
Radius = 12 inches / 2 = 6 inches = 0.1524 meters
A = π * (radius)^2 = 3.1416 * (0.1524)^2 = 0.0726 m^2
Substituting the values into the formula:
Thrust = 0.5 * 1.225 kg/m^3 * 0.0726 m^2 * (10 m/s)^2 *
0.1
Thrust ≈ 0.445 N
Lift Calculation:
Weight of drone = 1.05 kg

Substituting the values into the formula:
Lift = 0.5 * 1.225 kg/m^3 * (10 m/s)^2 * 0.0726 m^2 * 1
Lift ≈ 4.432 N
4. RESULT
Results of Sample Field Test for Weather Data Collection at
an Agricultural Site:
1) Humidity Data:
The weather drone recorded an average humidity level of
65% throughout the test period.
The humidity readings ranged from a minimum of 60%
during the early morning to a maximum of 70% during the
afternoon.
These variations in humidity levels indicated the natural
fluctuations in moisture content in the agricultural
environment.
Temperature Data:
The drone measured an average temperature of 25°C
during the test.
The temperature readings ranged between 22°C in the
early morning and 28°C in the mid-afternoon.
The recorded temperatures accurately represented the
diurnal temperature changes commonly observed in
agricultural settings.
Weather Patterns:
The collected data revealed a consistent pattern of higher
humidity levels in the early morning, gradually decreasing
throughout the day.
The temperature showed a corresponding increase as the
day progressed, peaking in the afternoon before gradually
cooling down in the evening.
These patterns align with typical weather trends observed
in agricultural regions.
Fig 2 humidity data
Fig 3 humidity data
5. CONCLUSION
In conclusion, the project has successfully addressed the
need for real-time weather data collection and
transmission for agricultural purposes. By designing and
developing a weather drone equipped with IoT
technology, the project aimed to provide farmers and
stakeholders with valuable insights into weather
conditions to enhance decision-making processes in
agriculture.
Overall, the project has demonstrated the potential of IoT-
enabled drones in revolutionizing weather data collection
for agricultural applications. The project's successful
implementation opens avenues for further research and
development in the field of precision agriculture,
empowering farmers with the tools they need to make
informed decisions and optimize their farming practices
based on real-time weather data.
REFERENCES
[1] Chen, S., Yu, Z., Li, X., & Wang, Z. (2018). An
Agricultural Weather Drone Monitoring System Based
on IoT Technology. IEEE Access, 6, 6651-6659.
[2] Li, Y., Geng, H., Qiao, X., & Feng, J. (2017). An IoT-
Based Autonomous Weather Monitoring System for
Weather Station Management. IEEE Internet of Things
Journal, 4(2), 369-377.
[3] Park, Y., Kim, Y., & Park, K. (2021). Development of
an Autonomous Weather Data Monitoring System
Using Drone-Based IoT. Sensors, 21(2), 647.
[4] Rizvi, S. S. S., Shah, M. A., & Khan, M. U. K. (2019).
Internet of Things-Based Weather Monitoring System
using Drones. In 2019 International Conference on
Electrical and Computing Technologies and
Applications (ICECTA) (pp. 1-6). IEEE.

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