ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Field Robotics
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1657567
This article is part of the Research TopicAutonomous Robotic Systems in Aquaculture: Research Challenges and Industry NeedsView all 5 articles
Avoidance behaviours of farmed Atlantic salmon (Salmo salar L.) to artificial sound and light: a case study of net-pen mariculture in Norway
Provisionally accepted- 1Norwegian University of Science and Technology, Trondheim, Norway
- 2SINTEF Ocean, Trondheim, Norway
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Intensive finfish aquaculture is increasingly relying on enabling technologies and solutions such as sensor systems, robotics, and other machinery. Together with conventional farming equipment, these systems may emanate acoustic noise and artificial light, impacting the pen environment. Farmed fish have been observed to respond behaviourally and/or physiologically to anthropogenic sounds and lights, indicating a stress reaction that could impair welfare and health. This study aimed to investigate how farmed Atlantic salmon respond to such stimuli, with direct implications for the design and operation of robotic and Zhang et al. mechanised systems in sea pens. We conducted experiments where we systematically exposed adult farmed Atlantic salmon in commercial net pens to sounds of frequencies within the range common to farm equipment (100-1000 Hz), and submerged lights at 8 and 12 m with four different intensities (600 lx to 14500 lx). Data was analysed using sonar data and a deep learning (DL) based method for processing that automatically identified fish distribution patterns and estimated the average avoidance distance to the sound/light source. The fish fled from the sound source while playing sounds of 400 Hz, while sounds at other frequencies did not elicit a response. The response to light intensity depended on deployment depth, with the fish moving closer to the source when intensity was increased at 8 m depth, but conversely moving further away with increasing density when it was placed at 12 m. These outcomes are important inputs for the design of equipment, autonomous vehicles, robotic interventions and operations at commercial farms to ensure that their sound and light emissions have minimal impact on the fish, thereby reducing the potential of induced stress.
Keywords: Aquaculture, Salmo salar, behaviour, deep learning, Robotics and Automation, fish farm design inputs
Received: 01 Jul 2025; Accepted: 26 Aug 2025.
Copyright: © 2025 Zhang, Bloecher, Danielsen Evjemo, Føre and Kelasidi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Martin Føre, Norwegian University of Science and Technology, Trondheim, Norway
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