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http://dx.doi.org/10.30693/SMJ.2022.11.2.39

A TabNet - Based System for Water Quality Prediction in Aquaculture  

Nguyen, Trong–Nghia (Department of AI Convergence, Chonnam National University)
Kim, Soo Hyung (Department of AI Convergence, Chonnam National University)
Do, Nhu-Tai (Department of AI Convergence, Chonnam National University)
Hong, Thai-Thi Ngoc (Department of Economics, Chonnam National University)
Yang, Hyung Jeong (Department of AI Convergence, Chonnam National University)
Lee, Guee Sang (Department of AI Convergence, Chonnam National University)
Publication Information
Smart Media Journal / v.11, no.2, 2022 , pp. 39-52 More about this Journal
Abstract
In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.
Keywords
Aquaculture; Artificial Intelligence; TabNet; Water Quality; Deep Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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