Abstract:
This study focuses on developing an Internet of Things (IoT)-based real-time monitoring and
control system for a laboratory-scale infrared paddy dryer to enhance the precision and efficiency
of the drying process. Traditional drying systems often lack real-time feedback mechanisms,
making it challenging to maintain optimal drying conditions, which can lead to over- or under
drying, energy inefficiencies and deterioration of grain quality. Integrating IoT technology
provides a promising approach for automating and optimizing drying operations through
continuous monitoring and controlling of key process variables. The developed system included
an ESP32 microcontroller as the primary unit for processing sensor data and controlling
operations. Temperature within the drying chamber was continuously monitored using a
DS18B20 digital temperature sensor and real-time feedback was used to regulate the drying
environment. Solid-state relay modules controlled the operation of the ceramic infrared heaters,
while a NEMA17 stepper motor drove the rotary feeding mechanism to achieve the desired final
moisture content. Sensor data were transmitted in real time to a cloud-based platform (Blynk),
enabling remote monitoring and analysis of drying conditions. A user-friendly web interface was
developed to display dynamic dashboards and generate automated alerts, supporting efficient
remote supervision and control of the drying process. The prototype was tested using freshly
harvested paddy with an initial moisture content of 22% (wet basis). During operation, the
system maintained the drying chamber temperature within ±2°C of the set range of 40°C to 80°C.
Moisture content reduction was evaluated across varying paddy feeding rates to assess system
responsiveness and consistency. Sensor calibration confirmed measurement accuracy, with
deviations remaining within acceptable limits (±0.4°C for temperature). Reliability tests indicated
stable data transmission over extended continuous operation, with no observed signal dropouts.
The proposed system establishes a foundation for real-time monitoring and control for intelligent
postharvest management, with scope for future integration of predictive control and energy
optimization.
