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Aquabyte WISE Welfare

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Sales planner
Performance
WISE Welfare

Insight into fish welfare creates added value

Aquabyte WISE Welfare is the first data-driven, automated system for scoring of fish welfare, built on the Institute of Marine Research’s LAKSVEL protocol. Beyond the welfare indicators defined in LAKSVEL, our system tracks additional metrics to provide deeper insights, enabling informed decisions based on a comprehensive view of fish welfare. This approach delivers added value for the business in both the short and long term.

In WISE, the general welfare status and superior rate per pen are displayed for easy comparison. A detailed view based on LAKSVEL and Aquabyte’s additional welfare indicators is also available.

Added value of using WISE Welfare

  • Less manual handling reduces stress and injuries to the fish
  • Health and welfare challenges are detected earlier
  • The fish’s welfare status becomes part of the decision-making process
  • Continuous overview of the superior rate at site and pen level
  • Easier to select the right pens and timing for harvest.
  • Best delousing method can be based on the fish’s welfare status

More than LAKSVEL

The welfare indicators in the LAKSVEL protocol form the foundation of our fish welfare monitoring product. To offer even deeper insights into the fish’s overall health, our system tracks several additional welfare indicators beyond those in LAKSVEL. With WISE Welfare, you receive extra data on eye condition, fin damage, gill damage, and jaw deformities. WISE Welfare also provides valuable information on whether wounds are active or healing.
These enhanced insights from WISE Welfare, beyond the LAKSVEL-defined indicators, are essential for making informed decisions that impact both fish welfare and company profitability.

Welfare scoring also in smolt production

Aquabyte WISE Welfare can be used to monitor fish in all types of pens and in smolt production. With automated welfare scoring in the smolt tank, the Aquabyte system reduces the need for manual handling. This results in less stress and fewer physical injuries for the smolt. Improved welfare and better health, due to reduced manual handling, bring benefits in the form of less disease and reduced mortality, both during the land-based production phase and when the smolt is transferred to sea.

Better decisions based on fish welfare

Aquabyte WISE Welfare delivers insights to support critical, well-informed decisions throughout the production process. Continuous updates on welfare status and trends enable preventive measures to be implemented at an earlier stage. Comprehensive knowledge of fish welfare is also essential when selecting the appropriate delousing method. Is the fish in suitable condition for mechanical delousing to be safe, or should an alternative treatment be considered?

Better overview

Aquabyte’s user portal makes it easy to gain an overview of the welfare status of each pen and compare them. General welfare data and the superior rate are displayed in a user-friendly interface that includes all pens equipped with the Aquabyte system. This interface provides a comprehensive view of the facility’s overall status and highlights any pens facing welfare-related challenges.
Detailed welfare information for each pen is available as real-time data and graphs showing status and trends over time. The images from the Aquabyte camera, which serve as the basis for welfare data calculations, are also accessible through the user portal.


he images captured by the camera in each pen are analyzed using machine learning. Both the images and any welfare indicators are displayed in Aquabyte’s user portal.

Which pen should be harvested first?

Selecting the wrong pen for harvest can have significant consequences for company profitability. Pens with fish of similar size can have vastly different potential when welfare status is considered. WISE Welfare provides insights into the status of wounds, injuries, and deformities, along with projections of expected developments over time. By making decisions based on knowledge and insight rather than assumptions, you increase the likelihood of better outcomes for both fish welfare and company profitability.

The following welfare indicators are monitored in WISE Welfare

  • Skin health (active and healing body wounds, scale loss, snout damage)
  • Deformities (gill cover deformity, upper and lower jaw deformity, spinal deformity)
  • Fin damage (anal fin, tail fin, pectoral fin, pelvic fin, dorsal fin)
  • Eye welfare (eye clouding, eye damage, bulging eyes)
  • Maturation
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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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