Precision farming in aquaculture: non-invasive monitoring of Atlantic salmon (Salmo salar) behaviour in response to environmental conditions in commercial sea cages for health and welfare assessment.

IF 2.9 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-04-23 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1574161
Meredith Burke, Dragana Nikolic, Pieter Fabry, Hemang Rishi, Trevor Telfer, Sonia Rey Planellas
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Abstract

Studies show that Atlantic salmon in captivity adjust their distribution in sea cages based on environmental gradients like temperature, waves, and photoperiod. This study used a computer vision algorithm at three marine farms to analyse fish group swimming behaviour termed "activity" (measured in percent), which includes fish abundance, speed, and shoal cohesion. The activity metric inferred the depth distribution of the main fish group and was analysed with respect to environmental conditions to explore potential behavioural drivers and used to assess changes in fish behaviour in response to a stressor, a storm event. During winter conditions, Farms A and B showed distinct thermal stratification, with fish activity demonstrating preference for the warmer lower water column (39.6 ± 15.3% and 27.5 ± 10.2%) over the upper water column (16.3 ± 5.7% and 18 ± 3.3%; p < 0.001). At Farm C, with thermally homogenous water, fish activity was similarly distributed between the upper (18.2 ± 6.9%) and lower (17.7 ± 7.6%) water column. Severe weather increased wave heights, influencing fish horizontal distribution differently at Farms B and C. At Farm B, a deeper site, fish remained in the warmer lower water column and avoided surface waves, while at Farm C, with shallower cages, they moved toward the side of the cage nearest the centre of the farm, presumably less exposed due to nearby cages. Understanding fish behavioural responses to environmental conditions can inform management practices, while using cameras with associated algorithms offers a powerful, non-invasive tool for continuously monitoring and safeguarding fish health and welfare.

水产养殖中的精准养殖:在商业海笼中对大西洋鲑鱼(Salmo salar)的行为进行非侵入性监测,以应对环境条件,用于健康和福利评估。
研究表明,圈养的大西洋鲑鱼会根据温度、波浪和光周期等环境梯度来调整它们在海笼中的分布。这项研究在三个海洋养殖场使用了计算机视觉算法来分析被称为“活动”的鱼群游泳行为(以百分比衡量),其中包括鱼类数量、速度和鱼群凝聚力。活动指标推断了主要鱼类群体的深度分布,并根据环境条件进行了分析,以探索潜在的行为驱动因素,并用于评估鱼类对压力源(风暴事件)的反应行为变化。在冬季条件下,养殖场A和养殖场B表现出明显的热分层,鱼类活动表现出对较温暖的下层水柱(39.6±15.3%和27.5±10.2%)的偏好,高于上层水柱(16.3±5.7%和18±3.3%);P < 0.001)。在C养殖场,采用热均质水,鱼类活动在上层水柱(18.2±6.9%)和下层水柱(17.7±7.6%)之间分布相似。恶劣的天气增加了波浪高度,不同程度地影响了B和C养殖场的鱼类水平分布。B养殖场位于较深的地点,鱼类停留在较温暖的较低水柱中,避开了表面波浪,而C养殖场的网箱较浅,它们向最靠近养殖场中心的网箱一侧移动,可能是因为附近有网箱,所以暴露在水面的鱼较少。了解鱼类对环境条件的行为反应可以为管理实践提供信息,而使用带有相关算法的摄像机为持续监测和保护鱼类健康和福利提供了强大的非侵入性工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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