University of Bergen (UiB) researchers have developed FishMet, a digital twin framework designed to support sustainable fish feeding by optimising growth, reducing feed waste, and improving fish welfare.
Photo: Andrea Magugliani
University of Bergen professors Ivar Rønnestad and researcher Sergei Budaev from the Department of Biological Sciences are developing FishMet, a proof-of-concept using digital twin models to explore smarter, more sustainable fish feeding strategies. It is exploring how years of marine physiology and behavioural research can be translated into tools for the aquaculture sector.
FROM LAB TO PROOF-OF-CONCEPT
FishMet originated from collaboration between UiB researchers and Vestlandets Innovasjonsselskap (VIS). The initiative centres on a digital twin model for precision aquaculture feeding strategies, aiming to help salmon and trout farming operations optimise feeding—reducing waste and improving fish welfare. Although still at a low Technology Readiness Level (TRL), the concept has been made available for exploratory licensing opportunities through VIS.
HOW THE DIGITAL TWIN WORKS
At its core, FishMet is a digital twin framework—a “virtual fish” that simulates appetite, digestion, metabolism, and growth by integrating biological and environmental data. Built on a conceptual model of neurophysiological feedback loops controlling fish appetite, FishMet is highlighted as offering a transparent, physiology-driven approach rather than relying solely on black-box machine learning methods.
The model can process inputs such as fish size, feed type and schedule, water temperature, oxygen levels, and behaviour data to estimate feed intake, gut transit, growth rates, feed conversion efficiency, and even stress or motivation indicators, according to the announcement. Designed as a modular, stochastic simulation, it can model individual fish or entire populations and is accessible via an open API and server-based deployment for testing decision-support applications.
“We aim to create a transparent digital salmon that combines AI with decades of biological knowledge, serving both as a research tool and a practical aquaculture predictor, especially in situations lacking data,” says Sergei Budaev.
FROM RESEARCH TO EARLY RESULTS
Over many years, Rønnestad and Budaev’s groups have conducted extensive experimental studies on the physiological mechanisms of appetite regulation—examining how gut-brain signalling, digestion rate, and neurohormones influence feeding. These insights form the scientific foundation of FishMet’s algorithms.
Initial pilot tests have shown promising predictive accuracy—estimating gut transit times in rainbow trout (Oncorhynchus mykiss) and growth performance in Atlantic salmon (Salmo salar). While further validation is needed, the potential benefits include reduced feed waste, improved growth efficiency, and lower environmental impact in salmon farming.
FUTURE POTENTIAL AND SUSTAINABILITY
FishMet remains an early-stage concept, but its biology-rooted transparency could give it advantages over opaque, purely data-driven systems, the university points out. Future development may expand its scope to additional fish species or life-stage transitions such as smoltification or maturation control.
INSPIRING RESEARCH TRANSLATION AT UIB
FishMet illustrates how fundamental university research, when paired with innovation-driven modelling and supportive structures like VIS, can begin a journey from academic insight to potential industry application. While still in the proof-of-concept stage, the project offers an example for other UiB researchers considering how their work might be translated into real-world solutions that support sustainable food production and blue growth.
