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Aquabyte

Welfare

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Sales planner
Performance
Welfare

Aquabyte WISE

Welfare Indicator ScorE –  Data-based decisions for increased revenue

WISE monitors and identifies all observable welfare indicators. The tool also provides continuous monitoring of the welfare status in the cage. You get real-time knowledge for all the welfare indicators you need. Skin, eye, deformities, fins and maturation. When you need it. 

You can choose indicators that are adapted to your needs. All year.

By having ongoing control of the welfare status, both the risk of reduced welfare and the risk of downgrading, due to poor welfare, are reduced. You also get information to be able to make data-based decisions on all central welfare indicators so that efficiency and income are increased.

Value for you:

  • Daily insight into welfare status in the pen to maintain high overall fish welfare
  • Reduced mortality risk
  • Reduced downgrading risk
  • Increased revenue

We have developed this solution in collaboration with the Institute of Marine Research. This has resulted in a method based on the Fishwell standard, where we have enabled “manual” scoring of fish through images. Through this collaboration, we have developed a scoring guide for image-based documentation of welfare.

WISE is based on the LaksVel standard, and we have collaborated with the Norwegian Institute of Marine Research in the development of this product. This has resulted in a method that is in accordance with the LaksVel standard, where we transfer “manual” scoring of fish to image-based scoring. Based on this collaboration, we have developed a scoring guide for image-based documentation of welfare.

Automatic LICE COUNTING

Aquabyte LICE monitors and counts lice levels without physically handling fish. Aquabyte LICE replaces the need for manual counting of salmon lice on site, and it can be used for weekly reporting to the Norwegian Food Safety Authority (Mattilsynet), following trends over time and detect changes in lice levels early. This provides a better basis for making good decisions and implementing targeted measures to combat salmon lice.

Value for you:

  • Accurate daily counting of sea lice across different life stages
  • Continuous surveillance of lice loads delivers key information for early decision making
  • Daily sea lice development gives insight into key trends
  • Reliable measurement of the effect of sea lice treatments and other sea lice initiatives
  • Reduced handling and fish stress

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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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