<![CDATA[The inefficiency of conventional irrigation systems causes water waste and suboptimal plant growth, particularly in smallholder farming contexts in Indonesia. This study implements an IoT-based plant monitoring and irrigation automation system using the ESP32 microcontroller integrated with soil moisture sensors, LDR light sensors, DHT11 temperature/humidity sensors, and an ultrasonic water level sensor. The system transmits data via the Wi-Fi protocol to the Firebase Realtime Database cloud platform and is displayed through an Android mobile application with automated push notification alerts. The research method used is Research and Development (R&D) with a prototyping approach, tested on tomato plants in a greenhouse for 12 weeks. Results show the system achieves a data transmission success rate of 97.3%, average sensor reading latency of 1.8 seconds, and soil moisture measurement accuracy of 96.1% compared to a standard tensiometer. Irrigation automation reduces water usage by 31.4% while increasing plant growth rate by 27.6% compared to manual irrigation. The system's novelty lies in the adaptive threshold mechanism that automatically adjusts optimal soil moisture parameters based on plant growth phase, without requiring manual reconfiguration by the user.]]>
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