Development of an IoT-Based Real-Time Water Quality Monitoring System for Fish Farming

Water quality monitoring is essential in aquaculture to ensure healthy fish growth and sustainable farming practices. In Malaysia, fish farmers commonly rely on manual methods to monitor key water quality parameters, including temperature and turbidity. However, these methods are labour-intensive, time-consuming, and prone to human error, resulting in delayed detection of water quality deterioration and inefficient data management. This study presents an Internet of Things (IoT)-based real-time water quality monitoring system to automate the monitoring process and improve aquaculture management. The system integrates an ESP32 microcontroller, a turbidity sensor, and a DS18B20 temperature sensor to continuously acquire water quality data. The collected data are transmitted wirelessly to the Blynk cloud platform for real-time monitoring, automatically recorded in Google Sheets, and visualized through Google Looker Studio to support historical data analysis. The developed prototype was evaluated through functionality testing to verify sensor connectivity, wireless communication, cloud synchronization, automated notifications, and data logging. The results demonstrate that the system successfully performs continuous data acquisition, real-time monitoring, cloud-based visualization, automated notifications, and historical data storage. The proposed system provides a practical and cost-effective solution for remote water quality monitoring in small-scale fish farming, supporting timely decision-making and more sustainable aquaculture management.

Internet of Things (IoT), Water Quality Monitoring, Aquaculture, Espressif Systems microcontroller with integrated Wi-Fi and Bluetooth (ESP32), Turbidity.

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