Open AccessAnalysis decision-making system for aquaculture water quality based on deep learning
Author(s) -
Jing Su,
Jiahong Chen,
Jiahong Wen,
Wende Xie,
Mingjie Lin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012028
Subject(s) - aquaculture , water quality , quality (philosophy) , visualization , computer science , big data , warning system , artificial intelligence , data mining , fishery , fish <actinopterygii> , ecology , telecommunications , philosophy , epistemology , biology
The quality of water is a key factor that determines whether aquaculture will be successful or not. Currently, aquaculture is mainly based on worker’s experience, which is not capable of precisely controlling the quality of the aquaculture water. The main function of the existing system for aquaculture water quality is still to manage water quality, but hardly to analyse or visualize the data of water quality. Furthermore, fewer works for prediction of future water quality are made. To tackle these issues, we design an analysis and decision-making system based on deep learning for aquaculture water quality. We combine the latest artificial intelligence and big data technology to achieve the functions of the system such as the visualization of data, the prediction of future water quality factors and early-warning of water quality, which helps the users to realize scientific aquaculture.