Design of an IoT-Based Monitoring and Control System for Catfish Cultivation Using Sugeno Fuzzy Logic

Water quality is a crucial factor in catfish farming because it directly affects fish growth and survival. However, manual water quality monitoring makes it difficult for farmers to detect changes in conditions quickly and accurately. This study aims to design and implement an Internet of Things (IoT)-based water quality monitoring and control system for ponds using Sugeno fuzzy logic to support efficient and adaptive catfish farming. The system is designed using an ESP32 microcontroller integrated with pH (PH405), temperature (DS18B20), TDS (SEN0244), and water level (HC-SR04) sensors. The system development method uses the Extreme Programming (XP) approach with planning, design, coding, and testing stages. The system displays sensor data in real-time through the Blynk application and a 20x4 I2C LCD, and stores data in the Firebase Realtime Database. Decisions on water drainage and refilling are made automatically based on the evaluation of pH and TDS parameters using Sugeno fuzzy logic. Test results show that the system is capable of automatically and accurately responding to unfavorable water conditions. This system has proven effective in continuously monitoring and controlling pond water quality, providing a practical solution for farmers to reduce the risk of fish mortality due to deteriorating water quality.