language
person_outline

Aquabyte BEHAVIOUR

Platform
Sales planner
Performance
WISE Welfare

More effective measures with early warnings and insights into fish behavior

Our BEHAVIOUR product monitors fish behaviour-related welfare. By measuring swim speed and swim tilt, BEHAVIOUR provides valuable insights into the conditions within the pen and can alert of changes in the fish’s overall welfare and stress levels. Speed and tilt are continuously monitored, with data updated hourly in the Aquabyte User Portal. BEHAVIOUR also automatically stores video clips from the pen, which can be shared with colleagues.

Benefits of using Aquabyte BEHAVIOUR

  • Simplified monitoring and recording of swim speed in submerged production
  • Early warnings of changes in fish stress levels
  • Early warnings of conditions that may lead to reduced growth, welfare challenges, and, over time, increased mortality
  • Access to video clips of fish with abnormal swim speed or swim tilt.

Implement measures earlier

Changes in fish behaviour can be early signs that something is happening in the pen. Such changes are often difficult to detect through standard observations from the pen edge. In closed or submerged production environments, the risk of behavioural changes being overlooked – whether caused by biological challenges or external factors – is significantly increased.

Aquabyte BEHAVIOUR monitors fish swim speed and swim tilt, providing updated data every hour. This enables early warnings of potential challenges, allowing for faster and more precise actions to prevent reduced fish welfare and increased mortality.

Data presented in the form of graphs provides valuable insights into changes in swim speed. Data from multiple pens can be displayed and compared within a single interface.

Swim speed

In BEHAVIOUR, swim speed data is presented both as daily averages and in graph form. Speed is measured in body lengths per second (BL/s), with abnormally high swim speeds highlighted using color codes, making it easier to detect and respond to critical changes.

The graphs make it easy to observe changes in swimming speed, which may be caused by elevated stress levels or welfare-related challenges. Changes in swim speed can also reveal shifts in biological or environmental factors that affect fish behaviour and welfare. Examples include jellyfish attacks, the presence of foreign fish, increased algae levels, or endringer in oxygen levels.

In submerged pens, where swim speed data needs to be documented, BEHAVIOUR will replace time-consuming and inaccurate manual measurements.

Graphs in the User Portal use color coding to highlight high swim tilt, making it easier to identify abnormal behaviour.

Swim tilt

Swim tilt monitors the fish’s horizontal tilt (the position and angle of the head relative to the direction of movement) while swimming. High tilt, where the fish’s head is consistently higher than the rest of its body, may indicate an inability to maintain proper buoyancy. A common cause of such behaviour is insufficient access to air, preventing the fish from refilling its swim bladder.

In open surface pens, refilling the swim bladder is usually not a challenge, as salmon can surface to do so. However, in submerged pens where salmon must obtain air from a dome, a sudden increase in tilt may indicate critical issues with air supply. The consequences of this can include reduced growth, welfare challenges, and, over time, an increased risk of mortality. In BEHAVIOUR, data on both swim speed and tilt is updated hourly, enabling early warnings if something is amiss.

Video files from the past 30 days are automatically stored in BEHAVIOUR and can easily be shared with colleagues who have access to the Aquabyte User Portal.

Automatically stores video snippets

BEHAVIOUR automatically stores four short video clips from each hour during data collection. These videos, available in the Aquabyte User Portal, can be used to verify changes and deviations in fish swim speed or tilt. The system archives clips from the past 30 days, allowing them to also document the effects of changes in water quality, welfare, and other critical parameters over time.

Through BEHAVIOUR, all stored videos can easily be shared with colleagues who have access to the Aquabyte User Portal.

Book demo

Q&A

Machine learning is a form of artificial intelligence. It is software, a network of algorithms, that is trained to recognize and interpret patterns in images and data sets. The technology behind it is the same that is used in facial recognition on social media, to analyze surroundings for self-driving cars – and to recommend movies and music on Spotify and Netflix based on your previous selections.

In a normal software, a calculation is performed on the basis of data that is entered into the software. In machine learning, software and algorithms evolve as they are “trained” with new datasets. This training becomes a form of pattern recognition, where the algorithms learn what to look for based on prior examples. As new training data and examples are provided to the machine learning software, the algorithms generate more and more precise results.

Let’s say that the algorithms are trained to recognize salmon lice. The algorithms are given training data, consisting of many images of salmon with annotations of lice in different stages. Through feedback in the form of these examples, the algorithms become increasingly accurate in recognizing salmon lice. It is important that the photos are taken under real conditions in the cage: changing lighting conditions, different angles of the fish, for example.

The quality of the algorithms is important. Algorithms may need to be adjusted along the way to emphasize biological factors in the data that make the results more accurate. For example, consider an algorithm that recognizes individual fish based on unique spot patterns. If algorithms are to recognize the same fish over time, these algorithms must also have the capacity to understand how these spot patterns change with the growth of the fish.

Images are taken with an underwater camera in the cage. Per Erik Hansen, Product Manager for Lice and Welfare at Aquabyte, mentions that the company uses a standard two-lens camera with particularly good optics to be able to measure 3D distance to the fish. The camera continuously takes images, and is placed in an optimal location such that as many fish as possible can swim past. Software running on the camera filters out unused images and analyzes the best ones.

“Customers who have used the system for a long time report that the results turn out to be in line with reality,” Per Erik Hansen continues. Farmers get daily lice numbers for each cage, with lice counts on far more fish than possible with manual counting. “The software distinguishes individual fish from each other, such that the same fish is only counted once. This contributes to Aquabyte delivering lice numbers with great accuracy,” notes Hansen.

Precise counting of salmon lice and accurate estimation of weight data and distribution are two use cases that have received fully developed solutions. Fish welfare is another important application area, including detection of winter sores, deformations, and other external changes on the fish. By giving the farmer much better insight and basis for decision making, it will be possible to improve daily operations, and in general achieve more efficient and sustainable fish farming.

keyboard_arrow_up