Application of machine learning in intelligent fish aquaculture: A review

被引：131

作者：

Zhao, Shili ^{[1
,2
]}

Zhang, Song ^{[1
,2
]}

Liu, Jincun ^{[1
,2
]}

Wang, He ^{[1
,2
]}

Zhu, Jia ^{[1
,2
]}

Li, Daoliang ^{[1
,2
,3
,4
,5
]}

Zhao, Ran ^{[1
,2
]}

机构：

[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

[2] China Agr Univ, Natl Innovat Ctr Digital Fishery, Beijing, Peoples R China

[3] China Agr Univ, Beijing Engn & Technol Res Ctr Internet Things Ag, Beijing 100083, Peoples R China

[4] China Agr Univ, China EU Ctr Informat & Commun Technol Agr, Beijing 100083, Peoples R China

[5] China Agr Univ, Minist Agr, Key Lab Agr Informat Acquisit Technol, Beijing 100083, Peoples R China

来源：

AQUACULTURE | 2021年 / 540卷

关键词：

Machine learning; Fish aquaculture; Fishery development; CONVOLUTIONAL NEURAL-NETWORK; DISSOLVED-OXYGEN CONTENT; COMPUTER-VISION; ARTIFICIAL-INTELLIGENCE; SPECIES CLASSIFICATION; FISHERIES MANAGEMENT; ACIPENSER-RUTHENUS; FEATURE-SELECTION; MASS ESTIMATION; MODEL;

D O I：

10.1016/j.aquaculture.2021.736724

中图分类号：

S9 [水产、渔业];

学科分类号：

0908 ;

摘要：

Among the background of developments in automation and intelligence, machine learning technology has been extensively applied in aquaculture in recent years, providing a new opportunity for the realization of digital fishery farming. In the present paper, the machine learning algorithms and techniques adopted in intelligent fish aquaculture in the past five years are expounded, and the application of machine learning in aquaculture is explored in detail, including the information evaluation of fish biomass, the identification and classification of fish, behavioral analysis and prediction of water quality parameters. Further, the application of machine learning algorithms in aquaculture is outlined, and the results are analyzed. Finally, several current problems in aquaculture are highlighted, and the development trend is considered.

引用

页数：19

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