IoT-Based Water Quality Monitoring System for Fish Ponds Using Fuzzy Inference Method

Abstract

This study uses the fuzzy inference method to develop an Internet of Things (IoT) system to monitor fish pond water quality. This system utilizes pH, Total Dissolved Solids (TDS), and temperature sensors to measure water quality parameters for fish health. Although many previous studies have discussed water quality monitoring, there are still limitations in applying IoT technology integrated with fuzzy inference methods for real-time data analysis. Many existing systems cannot provide information easily understood by fish farmers and are less accurate in measuring water quality parameters. Arduino Nano is the main microcontroller that processes sensor data, while the ESP8266 module is used for Wi-Fi connection for real-time monitoring through the thinger.io web-based application. Before testing, the sensors have been calibrated to ensure measurement accuracy. The test results on three water samples, namely tap water, tilapia pond water, and mujaer pond water, showed high accuracy and consistent results. The fuzzification results from the IoT device are close to the Simulink Fuzzy test results on each sample, with minor differences in tilapia pond water, likely caused by environmental factors such as aeration or sensor precision. This study aims to provide a system that is not only accurate but also presents data in a more understandable format so that it can help fish farmers make better pond management decisions. Thus, this study is expected to increase fish farming productivity through better and technology-based water quality management