Adaptive Fuzzy-IoT framework for real-time water quality regulation in Guppy Aquaculture

Water quality instability and inefficient manual monitoring are critical challenges in guppy fish cultivation that directly affect survival rates. To address these issues, this study presents Smart AquaLoop, an intelligent automated system designed to maintain water temperature and turbidity stability using an adaptive control mechanism. The research contribution is the development of a closed-loop control framework that integrates Fuzzy Logic with Internet of Things (IoT) to achieve real-time, autonomous environmental regulation. The methodology involves continuously monitoring temperature and turbidity via DS18B20 and TDS sensors connected to an ESP32 microcontroller. These inputs are processed using a Fuzzy Inference System to dynamically adjust the Pulse Width Modulation (PWM) duty cycles of the heater, cooling fan, and filtration pump. Experimental results demonstrate that the system effectively stabilizes water temperature within the optimal range of 25–28 °C despite external fluctuations. Furthermore, the system successfully reduces Total Dissolved Solids (TDS) levels from a turbid condition of approximately 500 ppm to a stable 300 ppm. In specific testing scenarios, the fuzzy controller accurately modulated the pump speed with a PWM value of around 102 to address high turbidity while keeping energy consumption efficient. The system also exhibited rapid response times in activating cooling or heating actuators based on precise fuzzy rule-based decisions. In conclusion, Smart AquaLoop significantly improves operational efficiency and minimizes fish stress, offering a robust solution for sustainable smart aquaculture.