Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

In aquaculture, controlling water quality parameters is an important challenge. The water quality parameters affect the growth of aquatic organisms. Thus, maintaining wat...Show More

Abstract:

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.
Published in: IEEE Sensors Journal ( Volume: 24, Issue: 3, 01 February 2024)
Page(s): 3811 - 3821
Date of Publication: 13 December 2023

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I. Introduction

With the impacts of the greenhouse effect, drastic climate changes have triggered a food shortage crisis. Crops depend on a suitable climate to grow, but drastic changes in the climate have affected crop yields. Consequently, to meet the large food demand of humans, the aquaculture industry has increasingly prospered in recent years because the aquatic products of aquaculture, such as fish, shrimp, and shellfish, have high nutritional value.

References

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