This project will use Artificial Intelligence (AI) and statistical models to aid the transition from a human experience-based management of RAS production to a knowledge-based automatic one. Feed management, feeding and feed waste is a major challenge to production. In marine aquaculture, video systems are widely used to observe the fish.
Norden og Baltikum styrker forskningen i akvakultur med ni nye forskningsprojekter, som blandt andet skal udvikle bæredygtigt foder til lakseopdræt. Dette er et vigtigt skridt på vejen til at gøre Norden til den mest bæredygtige region i verden inden 2030, lyder det fra NordForsks direktør.
The "Happy Salmon" project aims is to contribute with knowledge and solutions for a successful Atlantic salmon smolt production using novel sustainable feeds and that are applicable in modern recirculating landbased farming systems.
The RAS-TOOLS project will pave the way for the development of next generation RAS monitoring for better control of water quality and fish health issues, and thereby contribute to the development of a sustainable RAS industry.
SAFE project aims at utilizing the potential of oleaginous yeast and thraustochytrids and developing high-value oil enriched biomass containing carotenoids, astaxanthin and beta-glucans for salmon feed from wood-based materials.
The NON-Fôr project aims to build upon the state-of-the-art knowledge from previous EU and National projects and develop improved practices in feed manufacturing technology to promote the use of third generation ingredients in critical life stages of Atlantic salmon - startfeeding, during smoltification and in post smolt production.
The project will investigate economical aspects and regulatory constraints of introducing by-products into the human food chain by the bioconversion of these into microbial ingredients, and document environmental footprint of these ingredients for fish feed.
The DigiHeart project, a consortium of researchers and industry partners form Norway, Sweden and the Faroe Island will develop technology and control systems that use machine learning to continuously survey data about all these factors, in addition to operational and environmental conditions to identify causes behind heart disease and mortality in farmed salmonids.