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.

中文翻译：

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水产养殖鱼类行为量化指标与方法综述

在水产养殖中，鱼类行为监测和分析可以提供指导日常投喂、计划制定和疾病诊断所需的信息。机器视觉、生物记录器和声学系统等技术对于分析鱼类行为至关重要。本文重点介绍鱼类行为量化分析的工具和算法。目标是介绍他们的基本概念和原理，包括量化分析程序及其潜在的应用场景。本综述表明，最常见的行为量化指标可以分为三类：游泳指标、身体指标和情境指标。通常，游泳指数是研究人员最感兴趣的。然而，在鱼类行为分析中，实现信息的全面性和量化精度仍然具有挑战性。简而言之，本文旨在帮助研究人员和从业人员更好地了解当前最先进的行为量化分析，为智能育种的实施提供有力支持。