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Discover the opportunity in latent data

"Nothing is random in aquaculture. There is a reason for everything, fish mortality, lice attack, slow growth or sickness. It's just a matter of finding the root cause, and be able to predict the consequences."

- Aadne Tveit, VROC Norway

Digital Transformation of Aquaculture

Critical insights to improve fish farming efficiency

Sea and land-based fish farms are plagued with complex challenges effecting their efficiency, economic sustainability, and ultimately their ability to become a major contributor of global food security into the future. Utilising historical and real-time data, advanced analytics is key to providing insights to help optimize operations with the potential to help avoid sudden mass mortality, early identification of diseases, improved safety and reduced operating costs.

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Product Features

Why our customers choose VROC

VROC Predict Icon

Accurate Predictions

With model confidence and accuracy averaging 99%, users have confidence to make informed decisions

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Anytime, Anywhere

Optimize your team with anywhere, anytime access to real time monitoring and predictions

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Simple Data Storage

Data storage doesn’t have to be complicated or expensive

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Interoperability icon

Interoperability

Built to integrate with legacy systems and equipment agnostic so you can easily obtain insights from your data

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Smart Aquaculture Use Cases

Benefits to the aquaculture industry

Sudden mass fish mortality is a major concern for fish farms. Obtaining insights that can help predict an upcoming mass mortality can help operators intervene early. Operators may be able to schedule slaughtering or treat other causes, avoiding product loss. Applying advanced analytics to all available fish farm data may be able to provide operators with these critical insights.

The early identification of sea lice and other diseases in fish farms can provide farming operators with critical insights for early intervention to prevent the spreading of disease. Through holistic analysis of all available real-time data, AI can detect anomalies earlier than is possible with human analysis.

AI models can be produced to continuously monitor fish welfare. Aquaculturalists and operators can produce no-code AI models using the VROC model wizard to monitor, detect and alert them when conditions deviate from normal, so that settings can be adjusted to improve fish welfare.

The reliability and performance of critical machinery in the aquaculture industry can be improved through the use of AI for predictive maintenance. The AI models learn how the machines operate under different conditions and detect degradation, predicting failures before they happen. The advanced warning provides time for planned maintenance and the ordering of spares, helping to avoid any production downtime.

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Ai Implementation

Time to value realisation

Client Setup

Client supply of historical data and set-up of real-time streaming

Week 1

Data ingestion and real-time data streaming connection

Week 2 & 3

Client training and model generation

Week 4

Models in production. Client starts delivering business value

USEFUL RESOURCES

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