Project Modelling the relationship of exterior traits of fish

Projectnummer
LWV20386

Missie
Sleuteltechnologieën LWV

MMIP primair
Smart Technologies in Agri-Horti-Water-Food

MMIP secundair

Startdatum project
01-01-2021

Einddatum project
30-06-2025

Projectleider
John Bastiaansen

Website

Status project

Samenvatting project
In aquaculture, accurate real-time data on the health and performance of fish are essential for stabilizing
product quality and selecting the best fish in breeding programs. Collecting these data is costly, timeconsuming,
and stressful for the animals. Health and performance are affected by stress, hormones,
energy, fat metabolism and diseases. Modern breeding programs measure these traits because they impact
economically important production traits such as growth, survival and feed conversion rate (FCR).
Traditional measurements of health and metabolic state often require handling the animals or taking
invasive physical samples that require effort, money, and time. Consequently, phenotypic measures lag
the real-time development of the animal. Alternatively, image analysis may offer a fast, non-invasive and
labour reducing alternative. Previous studies have identified a link between fish metabolism and external
phenotypes but assessing the health and metabolic performance of fish using image analysis requires a
direct link between these traits and features of the image. In this project, we propose to investigate imageextracted
features as health and metabolic indicators and to develop predictive models for health and
metabolic status using real-time imaging. The project will combine appropriate fish images, cutting-edge
image analysis, and health condition and metabolic data as inputs for machine learning and deep learning
with artificial intelligence as decision support for breeding programs. The output is expected to reveal how
metabolism and health conditions interact and result in external phenotypes. The resulting models will be
applicable in high-throughput phenotyping for metabolic phenotype prediction, disease detection and
welfare assessment.

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