An AIoT-Based Water Quality Inspection System for Intelligent Aquaculture

In aquaculture, water quality has always been an important issue. Water quality will indirectly affect the growth of aquatic organisms, and maintaining the quality of water must become the primary goal. The traditional water quality prediction method not only has low accuracy but also consumes time and human resources. Therefore, to solve this problem, this paper proposes a water quality inspection system based on AIoT for intelligent aquaculture. The proposed system uses a deep learning model to infer the water quality environment to predict the data and, at the same time, to solve the sensor rod error caused by the growth of seawater algae. During the whole process, the data are transmitted to the background via WiFi, and the water quality of the aquaculture pond is monitored in real-time. The graph data are displayed on the web platform, allowing the user to browse the water quality data of the aquaculture pond easily.