FishMet: A Digital Twin Framework for Appetite, Feeding Decisions and Growth in Salmonid Fish DOI Creative Commons
Sergey Budaev, Giovanni Marco Cusimano, Ivar Rønnestad

et al.

Aquaculture Fish and Fisheries, Journal Year: 2025, Volume and Issue: 5(2)

Published: April 1, 2025

ABSTRACT Salmonids are important fish species in aquaculture countries the temperate zone. Optimisation of feeding next‐generation precision farming requires developing models for decision support and process control. Black box ML AI often very efficient but have drawbacks, such as requiring large amount training data reduced performance novel situations where no available. Thus, realistic appetite, decisions, feed intake, energetics growth is necessary. Such essential predicting performance, example, waste from uneaten faeces, growth, ‘what if’ scenario testing. We built a conceptual model based on review major neurophysiological mechanisms feedback loops controlling appetite food intake fish. Building this, we developed FishMet model: new extensible stochastic simulation framework that represents basic energy budget salmonid The advance, while bioenergetic part follows established theory. supported by server‐based components open API assimilation on‐demand execution allows to use digital twin. demonstrate relatively good prediction stomach gut digesta transit rainbow trout Oncorhynchus mykiss . twin also demonstrated efficiency pilot scale experiment Atlantic salmon Salmo salar discuss concept directions further development an applied predictive tool.

Language: Английский

Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review DOI Open Access
Yo‐Ping Huang, Simon Peter Khabusi

Processes, Journal Year: 2025, Volume and Issue: 13(1), P. 73 - 73

Published: Jan. 1, 2025

The integration of artificial intelligence (AI) and the internet things (IoT), known as (AIoT), is driving significant advancements in aquaculture industry, offering solutions to longstanding challenges related operational efficiency, sustainability, productivity. This review explores latest research studies AIoT within focusing on real-time environmental monitoring, data-driven decision-making, automation. IoT sensors deployed across systems continuously track critical parameters such temperature, pH, dissolved oxygen, salinity, fish behavior. AI algorithms process these data streams provide predictive insights into water quality management, disease detection, species identification, biomass estimation, optimized feeding strategies, among others. Much adoption advantageous various fronts, there are still numerous challenges, including high implementation costs, privacy concerns, need for scalable adaptable models diverse environments. also highlights future directions aquaculture, emphasizing potential hybrid models, improved scalability large-scale operations, sustainable resource management.

Language: Английский

Citations

4
FishMet: A Digital Twin Framework for Appetite, Feeding Decisions and Growth in Salmonid Fish DOI Creative Commons
Sergey Budaev, Giovanni Marco Cusimano, Ivar Rønnestad

et al.

Aquaculture Fish and Fisheries, Journal Year: 2025, Volume and Issue: 5(2)

Published: April 1, 2025

ABSTRACT Salmonids are important fish species in aquaculture countries the temperate zone. Optimisation of feeding next‐generation precision farming requires developing models for decision support and process control. Black box ML AI often very efficient but have drawbacks, such as requiring large amount training data reduced performance novel situations where no available. Thus, realistic appetite, decisions, feed intake, energetics growth is necessary. Such essential predicting performance, example, waste from uneaten faeces, growth, ‘what if’ scenario testing. We built a conceptual model based on review major neurophysiological mechanisms feedback loops controlling appetite food intake fish. Building this, we developed FishMet model: new extensible stochastic simulation framework that represents basic energy budget salmonid The advance, while bioenergetic part follows established theory. supported by server‐based components open API assimilation on‐demand execution allows to use digital twin. demonstrate relatively good prediction stomach gut digesta transit rainbow trout Oncorhynchus mykiss . twin also demonstrated efficiency pilot scale experiment Atlantic salmon Salmo salar discuss concept directions further development an applied predictive tool.

Language: Английский

Citations

0