Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture

In aquaculture, controlling water quality parameters is an important challenge. The water quality parameters affect the growth of aquatic organisms. Thus, maintaining water quality balance has become the primary goal of aquaculture operators. However, the traditional water quality inspection method is low in accuracy and consumes considerable time and human resources. On the other hand, since water quality sensors are immersed in seawater for a long time, algae will grow on the sensors, affecting their accuracy. Therefore, to solve the abovementioned problems, this article reports the design and implementation of a full-time artificial intelligence of things (AIoT)-based water quality inspection and prediction system, which uses a simple recurrent unit (SRU) model to predict water quality data. With the proposed system, it is possible to collect water quality sensing data 24 h a day and further use the SRU model for sensor data prediction to assist aquaculture farmers in managing and controlling outdoor aquaculture ponds. Moreover, a 24-h water quality sampling tank is designed to overcome the problem of sensor error. Throughout the whole process, data are transmitted to a water quality monitoring cloud platform for further inspection and prediction. In this article, SRU-based prediction is used to obtain predictions of water quality parameters, and three popular metrics mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance. As a result, experimental results show that the proposed method offers good performance for prediction of water quality.