鱼群行为定量方法研究进展

IF 8.8 1区 农林科学 Q1 FISHERIES
Yaoguang Wei, Lin Ji, Dong An
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引用次数: 0

摘要

在水产养殖中,鱼群行为的定量分析是指系统地应用数学和统计工具,通过计量、统计和建模等手段,对鱼群行为特征进行精确的测量和描述。与个体行为的研究相比,鱼群行为的分析对于管理鱼类健康和提高养殖效率至关重要。定量分析加深了我们对鱼群结构和相互作用模式的理解,有助于制定更合理、更有效的喂养策略。传统的人工检测方法耗时长、劳动强度大,且准确性有限,无法对鱼群进行定量分析,难以对鱼群的行为和生理状态进行参数化评估,给准确评估带来挑战。然而,近年来,随着新技术和量化指标的出现,对鱼群行为的评估变得更加准确和客观。本文综述了定量分析鱼群行为的三种关键技术:计算机视觉、声学和传感器。它概述了三种类型的定量指标:行为,生物量估计和环境。此外,它还提供了对鱼群行为对四个关键因素的反应的见解:环境压力、摄食、疾病和繁殖。研究表明,综合的行为识别信息往往需要根据养殖场的具体需求和条件选择合适的技术或整合多种技术。因此,未来对多模态数据融合的研究可能有助于水产养殖领域的进一步发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review on Quantitative Methods of Fish School Behaviors

In aquaculture, the quantitative analysis of fish school behavior refers to the systematic application of mathematical and statistical tools for the precise measurement and description of fish school behavior characteristics through metrics, statistics, and modeling. Compared to studies on individual behavior, the analysis of fish school behavior is crucial for managing fish health and enhancing aquaculture efficiency. Quantitative analysis deepens our understanding of fish school structure and interaction patterns, facilitating the development of more rational and efficient feeding strategies. Traditional manual detection methods are time-consuming, labor-intensive, and have limited accuracy, resulting in inadequate quantitative analysis of fish schools and difficulties in parametrically assessing their behavior and physiological states, which pose challenges to accurate evaluations. However, in recent years, with the emergence of new technologies and quantification indicators, the assessment of fish school behavior has become more accurate and objective. This review summarizes three key technologies for quantitatively analyzing fish school behavior: computer vision, acoustics, and sensors. It outlines three types of quantitative indicators: behavior, biomass estimation, and environment. Furthermore, it provides insights into the response of fish school behavior to four key factors: environmental stress, feeding, disease, and reproduction. The study indicates that comprehensive behavior recognition information often requires selecting suitable technologies or integrating multiple technologies based on the specific needs and conditions of the aquaculture site. Therefore, future research in multimodal data fusion will likely contribute to further advancements in the field of aquaculture.

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来源期刊
CiteScore
24.80
自引率
5.80%
发文量
109
审稿时长
>12 weeks
期刊介绍: Reviews in Aquaculture is a journal that aims to provide a platform for reviews on various aspects of aquaculture science, techniques, policies, and planning. The journal publishes fully peer-reviewed review articles on topics including global, regional, and national production and market trends in aquaculture, advancements in aquaculture practices and technology, interactions between aquaculture and the environment, indigenous and alien species in aquaculture, genetics and its relation to aquaculture, as well as aquaculture product quality and traceability. The journal is indexed and abstracted in several databases including AgBiotech News & Information (CABI), AgBiotechNet, Agricultural Engineering Abstracts, Environment Index (EBSCO Publishing), SCOPUS (Elsevier), and Web of Science (Clarivate Analytics) among others.
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