In aquaculture, accurate and automatic quantification of fish behaviour can provide useful data input for production management and decision-making. In recent years, with the focus on fish welfare, it has become urgent to study and use nondestructive quantitative methods of fish behaviour in aquaculture. In this paper, based on the literature of the past 30 years, nonintrusive and automatic quantitative methods for fish behaviour are analysed. Firstly, several important fish behaviours in aquaculture are listed, and the quantification of fish behaviour is summarized in four stages: detection, tracking, feature extraction and behaviour recognition. Then, nonintrusive methods of fish behaviour quantification, through machine vision, acoustics and sensors, and their advantages and disadvantages are also compared and discussed in detail. It is concluded that the combination of multiple methods and deep learning is a key technology for fish behaviour quantification, which has gradually become a popular focus of research and application in recent years. This review can be used as a reference to improve fish behaviour quantification in future, so as to create a more effective and economic technical method.

中文翻译：

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水产养殖鱼类行为的非侵入式自动定量分析方法

在水产养殖中，鱼类行为的准确和自动量化可以为生产管理和决策提供有用的数据输入。近年来，随着对鱼类福利的关注，研究和使用水产养殖中鱼类行为的无损定量方法变得紧迫。本文在过去30年文献的基础上，对鱼类行为的非侵入性和自动定量方法进行了分析。首先列出了水产养殖中几种重要的鱼类行为，将鱼类行为的量化概括为四个阶段：检测、跟踪、特征提取和行为识别。然后，还详细比较和讨论了通过机器视觉、声学和传感器对鱼类行为进行量化的非侵入式方法，以及它们的优缺点。得出的结论是，多种方法与深度学习相结合是鱼类行为量化的关键技术，近年来逐渐成为研究和应用的热门热点。该综述可作为今后改进鱼类行为量化的参考，从而创造一种更有效、更经济的技术方法。