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A survey of fish behaviour quantification indexes and methods in aquaculture
Author(s) -
An Dong,
Huang Jinze,
Wei Yaoguang
Publication year - 2021
Publication title -
reviews in aquaculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.998
H-Index - 47
eISSN - 1753-5131
pISSN - 1753-5123
DOI - 10.1111/raq.12564
Subject(s) - aquaculture , fish <actinopterygii> , context (archaeology) , schedule , computer science , data science , risk analysis (engineering) , fishery , biology , business , paleontology , operating system
Abstract In aquaculture, fish behaviour monitoring and analysis can provide the information required to guide daily feeding, schedule making and disease diagnosis. Technology such as machine vision, bio‐loggers and acoustic systems is essential to analyse fish behaviour. This paper focuses on tools and algorithms for fish behaviour quantification analysis. The goal is to present their basic concepts and principles, including the quantification analysis procedure and its potential application scenarios. This review shows that the most common behaviour quantification indexes can be categorised into three classes: swimming indexes, physical indexes and context indexes. Typically, swimming indexes are of the most interest to researchers. However, achieving comprehensiveness of the information and quantisation precision remain challenging in fish behaviour analysis. In brief, this paper aims to help researchers and practitioners better understand the current state‐of‐the‐art behavioural quantification analysis, which provides strong support for the implementation of intelligent breeding.