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Aquabyte BEHAVIOUR

Platform
Sales planner
Performance
WISE Welfare

Early warning and critical decision support

Aquabyte BEHAVIOUR is a valuable tool that provides detailed insight into fish behaviour and deviations from normal patterns. It serves as an alert system, enabling early identification of challenges and offering crucial decision support when measures for safe production and improved fish welfare need to be implemented.

Aquabyte BEHAVIOUR – data and video for enhanced insights

In Aquabyte BEHAVIOUR, behavioural changes over time are displayed as graphs.

Small behavioural changes can provide important signals and warnings that something is about to happen but can be difficult to detect through regular observations. This is especially true in closed or submerged production environments, where the fish are out of sight.

Aquabyte BEHAVIOUR monitors fish swim speed, swim tilt, and breathing index, delivering precise and regular data through Aquabyte’s user portal. The BEHAVIOUR data points are supplemented with video clips from the pen, which remain available for three months after recording. By combining data and video from BEHAVIOUR with our weight, lice, and welfare products – and environmental data from sensors – fish farmers gain a complete picture of the fish’s status and development over time.

Benefits of using Aquabyte BEHAVIOUR

  • Better monitoring and tracking of fish behaviour
  • Early warnings of increased stress, welfare challenges, gill health issues, and conditions that may lead to higher mortality
  • Simplifies registering of swim speed in submerged production
  • Video clips make it easier to confirm behavioural changes in fish and identify their causes.
  • BEHAVIOUR, together with our other products, provides a complete picture of the fish’s status and development over time.
Aquabyte BEHAVIOUR monitors fish swim speed, swim tilt, and breathing index – and automatically stores video clips from the pen in a three-month archive.

Breathing index – insights into the fish’s gill health and condition

The breathing index measures how often the fish opens its mouth to channel water through the gills, providing valuable insights into gill health and overall condition. Issues with oxygen uptake caused by disease or low oxygen levels in the water can be detected by monitoring changes in the fish’s breathing index.

Swim speed – an indicator of fish status and stress levels

BEHAVIOUR provides accurate measurements of fish swim speed and indicates whether the fish is swimming faster than it should over time. Increased swim speed is a sign of stress, and speed data can help uncover biological or environmental factors affecting the fish – such as jellyfish attacks, foreign fish presence, increased algae levels, and changes in oxygen levels.

In submerged production, there is a requirement to register fish swim speed. Aquabyte BEHAVIOUR does this automatically, contributing to a more efficient and less labor-intensive process.

Swim tilt – monitors the fish’s access to air

Swim tilt monitors the fish’s head position and angle relative to its swimming direction. Increased tilt, where the fish consistently holds its head higher than its body, may indicate that it is not refilling its swim bladder, preventing it from maintaining proper buoyancy.

In open pens, fish swim to the surface to refill their swim bladder. In submerged production, they rely on air supplied through the air dome in the pen. Indreased swim tilt can indicate issues with air supply, and monitoring this behavior provides early warnings of technical problems related to it.

Three-month video archive – documentation and decision support

Aquabyte BEHAVIOUR automatically records video clips from the pen and stores them in an archive. The clips are time-stamped and available for three months after recording. With a comprehensive video history, abnormal behaviour can be observed, and the footage can be used to document the effects of biological and environmental changes over time. The video clips in the archive can be shared with colleagues who have access to the Aquabyte User Portal.

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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.

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