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Deep learning for smart fish farming: applications, opportunities and challenges

文献类型： 外文期刊

作者： Yang, Xinting 1 ; Zhang, Song 1 ; Liu, Jintao 1 ; Gao, Qinfeng 6 ; Dong, Shuanglin 6 ; Zhou, Chao 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China

4.Tianjin Univ Sci & Technol, Tianjin, Peoples R China

5.Univ Almeria, Dept Comp Sci, Almeria, Spain

6.Ocean Univ China, Minist Educ, Key Lab Mariculture, Qingdao, Shandong, Peoples R China

关键词： advanced analytics; aquaculture; deep learning; smart fish farming

期刊名称：REVIEWS IN AQUACULTURE （ 影响因子：10.592； 五年影响因子：10.455 ）

ISSN： 1753-5123

年卷期：

页码：

收录情况： SCI

摘要： The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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