A preliminary study of IoT applied in indoor aquaculture system

The internet of things technology (IoT) is growing rapidly. IoT implementation has been conducted in several sectors. One of them is aquaculture. Traditional farmers face problems with monitoring water quality and the way to increase the quality of the water quickly and efficiently. The intelligent fish farm tries to deal with the precise work of optimizing critical parameters on the water pond, which is dissolved oxygen. The continuous monitoring and measurement of water parameters aim to ensure the sustainability of fish production. Besides, problems with insufficient dissolved oxygen and the accumulation of potentially toxic nitrogenous waste products are universally encountered in aquaculture ponds. They ultimately limit the production that can be profitably achieved. In this paper, we aim to investigate the relationship between Final Dissolved Oxygen levels with other water parameters, find out which parameters significantly affect Final Dissolved Oxygen levels, and build a prediction model by using 1043 real-Time data from the indoor fish pond IoT-based monitoring system using a multi-sensor board with interconnected sensors for all necessary parameters, including temperature, salinity, pH, ORP, turbidity, chlorophyll, ammonia nitrate. A systematic correlation and regression study showed a significant linear relationship among different pairs of water quality parameters. As a result, we found their relationship, water parameters that significantly affect Dissolved Oxygen as a critical parameter in the fish pond, and the prediction model. © 2024 Author(s).