Smart IoT-based water quality control for lobster aquaculture using Mamdani fuzzy logic

Lobster is a fishery commodity with high economic value and increasing global market demand. In Indonesia, especially in Sidoarjo Regency, there is great potential for its development. However, commonly used cultivation practices still face challenges such as unstable water quality, inefficient feeding, and the lack of an accurate data-based harvest prediction system. This research offers a solution in the form of developing an integrated Smart Aquaculture system based on the Internet of Things (IoT) and artificial intelligence (AI) to support more modern and sustainable lobster cultivation. The system is designed using an ESP32 microcontroller connected to a pH sensor (PH-4502C), a turbidity sensor, and a DS18B20 temperature sensor to monitor water quality in real time, as well as a relay module to automatically control the drain and purifier pumps using the Mamdani Fuzzy Logic method. The system is also integrated with a website, making it easier for farmers to monitor and control it remotely. A 24-hour field test demonstrated that the system accurately responded to changes in environmental parameters, maintained optimal water quality, and produced output consistent with theoretical calculations with a 0% error rate, with a 15-minute drain and purifier pump operation time. Overall, the proposed system demonstrates high reliability in maintaining optimal water quality, reducing lobster mortality risks, and improving operational efficiency. These results indicate that the system provides a solid foundation for future integration of automated feeding and data-driven harvest prediction modules.