Performance Evaluation of a Smart Aeration System for Tilapia Farming Based on IoT and Environmental Sensing - Firmansyah maulana sugiartana Nursuwars; Rahmi Shofa; Nurul hiron; Ida Bagus Alit Swamardika; aceng sambas (Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia, 2025)
Fluctuations in dissolved oxygen (DO) levels in high-density biofloc-based tilapia aquaculture pose a critical challenge that directly affects fish growth, survival rate, and feed conversion efficiency. Traditional aeration systems that operate continuously are energy inefficient and unable to adapt dynamically to real-time environmental variations. This study aims to improve DO stability and energy efficiency in biofloc-based tilapia aquaculture through adaptive aeration control. This study designs and evaluates an Internet of Things (IoT)-based smart aeration system that automatically regulates aeration intensity based on real-time DO sensing and threshold-based control logic. The system is built on an ESP32 microcontroller integrated with a digital DO sensor, a water temperature sensor, and relay actuators for blower control, with data transmission via the MQTT protocol and real-time monitoring through a web-based dashboard. Experimental testing was conducted for seven days in a biofloc pond containing 200 tilapia, with a comparative analysis between manual and automated control modes. The results demonstrate that the smart aeration system effectively maintained DO within the optimal range of 5.1–6.8 mg/L while reducing blower energy consumption by 26.7%. Communication reliability was validated with an average transmission delay of 740 ms and a packet loss rate of 1.8%, both of which are acceptable for real-time IoT applications. Data analysis showed consistent improvements in DO stability and energy efficiency throughout the experimental stage. In addition, the system’s modular architecture enables scalability for integration with additional sensors or renewable energy sources, such as solar panels, to support off-grid operations. The findings affirm that the proposed system offers a practical, low-cost, and sustainable solution for data-driven aquaculture management and contribute to the advancement of smart, environmentally responsive aquaculture systems.