Smart IoT-Based Broiler Room Controller: Design, Implementation, Performance Evaluation, and Optimization

This study presents the creation and performance assessment of an Internet of Things (IoT) smart broiler room controller. In order to monitor and regulate vital environmental parameters like temperature and humidity and guarantee ideal circumstances for broiler development, the system combines an ESP32 microcontroller with the Arduino platform. The system creates the perfect broiler-growing environment by dynamically adjusting fan, heater, and light settings based on real-time data using sensors like the DHT11 and LDR. We conducted a comprehensive performance study over seven weeks, comparing sensor values (temperature from the DHT11 and thermometer, and humidity from the DHT11 and hygrometer) with reference standards. With an average temperature of 29.99°C and a humidity level of approximately 39.95%, the results demonstrated steady temperature regulation and consistent, albeit marginally poor, humidity control. We found problems with the system's humidity management mechanism, which relied on a fan and heater and lacked sufficient accuracy. The absence of a real-time clock (RTC) module also impacted scheduling consistency, and the remote interface's lack of security features increased the risk of unwanted access. Adding an RTC module for improved scheduling, incorporating a humidifier for more accurate humidity control, and implementing a secure login mechanism to improve remote interface security were some of the suggestions offered to optimize the system. Additionally, using platforms like PlatformIO and MicroPython instead of the Arduino IDE could streamline code management and boost system performance. Additionally, the report recommends power consumption optimization techniques, such as using MQTT communication protocols and integrating solar power. In order to suggest strategies for system optimization, this study reuses elements of the experimental data, methodology, figures, tables, and implementation from our previous work [1], along with additional text and code included in the methodology. Through increased accuracy, sustainability, and efficiency, the suggested techniques seek to strengthen the system's performance as a reliable broiler farming solution.