A TabNet - Based System for Water Quality Prediction in Aquaculture

• 발행기관: 한국스마트미디어학회
• 저자명: Trong-Nghia Nguyen 김수형(Soo Hyung Kim) 도누따이(Nhu-Tai Do) Thai-Thi Ngoc Hong 양형정(Hyung Jeong Yang) 이귀상(Guee Sang Lee)
• 간행물 정보: 『스마트미디어저널』Vol11, No.2, 39~52쪽, 전체 14쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2022.03.30

영문 초록

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.

목차

I. INTRODUCTION

II. RELATED WORK

III. PROPOSED METHOD

IV. EXPERIMENT AND RESULTS

V. CONCLUSION

키워드

Aquaculture Artificial Intelligence TabNet Water Quality Deep Learning