Design and Implementation of an IoT-Based Real-Time Monitoring and Closed-Loop Control System for KUB Day-Old Chick Brooding Houses
Abstract
The brooding phase represents the most critical physiological window in the life cycle of Kampung Unggul Balitbangtan (KUB) native chickens. Day-Old Chicks (DOC) possess underdeveloped thermoregulatory mechanisms, requiring strict microclimatic boundary control within 32.0–35.0 °C and 60–70% relative humidity (RH) to prevent hypothermia, respiratory distress, and high mortality. Traditional brooding management relies predominantly on manual observation and unmodulated incandescent heating, resulting in severe thermal fluctuations, excessive fuel/electricity consumption, and uneven chick growth. This study presents the design, fabrication, and rigorous empirical validation of an Internet of Things (IoT)-driven precision brooding environment system equipped with automated closed-loop multi-actuator control. The architecture integrates a high-precision digital DHT20 I2C temperature and relative humidity sensor, an analog DC voltage sensor for 12V DC power supply rail integrity monitoring, an ESP32 dual-core microcontroller running a deterministic hysteresis-PID control algorithm, and dynamic cloud telemetry via the Blynk IoT protocol. Static calibration against secondary standards (Fluke 971 and Keysight 34461A) demonstrated exceptional sensor reliability, yielding an absolute temperature error of 0.18 ± 0.07 °C (R² = 0.998) and voltage linearity across 9.0–15.0 V with a maximum deviation of 0.04 V (R² = 0.999). Dynamic 24-hour testing under fluctuating tropical ambient conditions (22.8–33.4 °C; 58.2–94.1% RH) confirmed that the closed-loop system robustly maintained internal brooder temperature at 33.52 ± 0.38 °C and relative humidity at 65.4 ± 2.8%, eliminating nocturnal thermal stress. Biological validation across a 14-day brooding trial with n = 200 KUB DOC chicks showed a significant reduction in cumulative mortality from 8.0% (conventional brooder) to 1.0% (IoT brooder), alongside a 34.1% improvement in mean Body Weight Gain (331.5 ± 12.4 g vs. 247.2 ± 15.8 g) and an optimized Feed Conversion Ratio (FCR) of 1.24 vs. 1.62. The proposed cyber-physical system offers an energy-efficient, robust, and scalable automation paradigm for smart poultry farming under tropical climatic volatility.