Abstract:
In the light of increasing global challenges such as climate change, rapid
urbanization, and the growing demand for food due to population rise, there is an
urgent need to implement sustainable and efficient agricultural practices. One
promising solution is the application of smart farming technologies that utilize realtime
data for decision-making. This project, conducted at KCAEFT, Tavanur
between January and May 2025, focuses on the development and deployment of a
real-time microclimate monitoring system inside a polyhouse using IoT-embedded
sensor technologies. The system is built around a microcontroller-based architecture
with ESP32 modules, enabling wireless communication and real-time data
visualization. Multiple environmental parameters critical for crop production—such
as air temperature, relative humidity, soil temperature, light intensity, and wind
speed—were continuously monitored using a network of strategically placed
sensors. Data collected was transmitted to an integrated cloud platform, allowing for
remote access and timely insights without manual supervision. To ensure accuracy
and reliability, sensor calibration was carried out through comparative analysis with
standard instruments and digital weather applications such as Wind Compass Pro and
Zoom Earth. Though some sensors (e.g., anemometer, soil temperature, and BH1750
light sensor) initially showed minor deviations of recorded data from their true
values, the calibration process yielded highly satisfactory results, as reflected by high
R² values, validating their performance. A well-planned wiring layout ensured
minimal energy usage and cost-efficiency, with total wire requirements of 842 meters
per module. Visualization tools and analytics helped track trends in microclimatic
variations, enabling precise climate monitoring. This project demonstrates a scalable,
low-cost, and energy-efficient solution for real-time microclimatic monitoring of
polyhouse environments. It provides a robust foundation for future automation,
paving the way for smart farming systems that optimize resource usage, reduce
labour dependency, and enhance productivity through precision agriculture practices
