Internet of things-based water quality monitoring design to improve freshwater lobster farming management - Muthmainnah, Muthmainnah; Khasanah, Iva Khuzaini; Hananto, Farid Samsu; Romadani, Arista; Tazi, Imam; Mulyono, Agus; Tirono, Mokhamad (International Journal of Electrical and Computer Engineering (IJECE), 2025)
The development of lobster farming requires careful water quality monitoring to ensure optimal growth and health. This study introduces a novel internet of things (IoT)-based water quality monitoring system designed specifically for lobster farming applications, operating on the Antares IoT platform. The system incorporates pH, temperature, and turbidity sensors to measure critical water quality parameters. The sensors were calibrated and validated using standard methods, yielding high accuracy, with average values of 98.74% for pH, 98.78% for temperature, and 98.56% for turbidity. The study also involved direct monitoring over five days, with pH values ranging between 8-10, temperatures between 23-27°C, and stable turbidity at 90-99 NTU. The novelty of this system lies in its ability to provide real-time, reliable data and predictive analysis to support effective water quality management in lobster farming. Unlike traditional water quality monitoring systems that lack real-time data analysis or predictive capabilities, this system integrates both monitoring and forecasting features, allowing for more proactive management. Additionally, it offers higher accuracy and lower sensor drift compared to older, manual water quality monitoring methods. Experimental results indicate that the proposed monitoring system can deliver accurate and reliable data, supporting optimal farming conditions. These findings align with and expand upon existing literature, offering a more integrated and efficient solution for real-time and accurate monitoring in lobster farming.