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Aquabyte WEIGHT

Platform
Sales planner
Performance
WISE Welfare


Accurate data on biomass, weight, and growth supports better decision-making

With accurate calculations of weight and biomass, the insights from Aquabyte WEIGHT serve as a decision-making foundation when critical choices must be made during production. The system provides updated data daily, making it easy to track developments in biomass, weight, weight distribution, and K-factor. Weight forecasts up to two weeks ahead, based on historical and environmental data, enable Aquabyte WEIGHT to generate detailed and accurate harvest reports.

Displaying average weight, growth, number of fish, and total biomass makes it easy to compare the status between pens

Benefits of using Aquabyte WEIGHT

  • Automated weight calculations reduce stress and injury to fish
  • Key decisions can be made based on insights rather than assumptions
  • Feeding and feeding strategy can be optimized according to actual weight and growth rate
  • Accurate harvest reports up to two weeks before fish harvesting
  • Better predictability in future weight development and profitability

Weight calculation based on images and machine learning

The weight calculations performed by Aquabyte WEIGHT are based on thousands of images taken daily by the Aquabyte camera within the pen. These images are analyzed using artificial intelligence and machine learning, providing precise calculations of key factors such as weight, growth, and biomass. The data retrieved from the pens is presented in the Aquabyte User Portal.

Trendlines for average weight, K-factor, and CV for one or more pens can be viewed with data up to 12 months back.

Overview with trendlines and graphs

Data on average weight, growth, fish population and total biomass are presented to provide an easy overview of each individual pen and allow status comparisons across different pens and sites. Detailed information on the development of average weight, K-factor, and coefficient of variation (CV) is displayed in graphs with data going back up to 12 months. Weight distribution in actual weight is also shown as bar charts, either for a single pen or multiple pens.

All weight calculations, graphs, and charts are updated daily in the Aquabyte User Portal. Together, they provide insights into both current status and trends over time, serving as a valuable decision support tool when critical choices need to be made during production.

Optimize feeding

The daily updates provided by Aquabyte WEIGHT are an excellent tool for optimizing feeding. Accurate data on biomass, weight distribution, and growth development provide insights into the fish’s actual status, allowing for early detection and correction of over- or underfeeding. Optimized feeding based on accurate weight data saves the company significant expenses and creates real value for the business in both the short and long term.

Accurate harvest reports

With two-week growth forecasts, Aquabyte WEIGHT generates highly accurate harvest reports, creating predictability for both the site and the harvesting plant receiving the fish. The harvest reports are based on data from the production period, the last day of feeding, water temperature, and other factors that impact fish growth in the upcoming period. The reports calculate round weight, HOG weight, and estimate weight distribution within the pen.

When Aquabyte WEIGHT is combined with Aquabyte WISE Welfare, the harvest reports also provide information on the estimated percentage of superior-grade fish per pen.

Custom downgrading criteria

Aquabyte WEIGHT allows for defining custom downgrading criteria for harvest reports. Criteria can be set according to company standards or tailored to the specific requirements of the harvesting plant receiving the fish. Downgrading criteria are saved in Aquabyte WEIGHT and can be selected for each individual harvest report to be created.

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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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