
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 18, 2025
Abstract Measuring and monitoring fish welfare in aquaculture research relies on the use of outcome- (biotic) input-based (e.g., abiotic) indicators (WIs). Incorporating behavioural auditing into this toolbox can sometimes be challenging because sourcing quantitative data is often labour intensive it a time-consuming process. Digitalization process via computer vision artificial intelligence help automate streamline procedure, gather continuous optimisation assist decision-making. The tool introduced study (1) adapts DeepLabCut framework, based machine learning, to obtain pose estimation Atlantic salmon parr under replicated experimental conditions, (2) quantifies spatial distribution through metrics inspired by ecological concepts home range core area, (3) applies inspect variability around feeding. This proof concept demonstrates potential our methodology for automating analysis behaviour relation including detection, variations within between tanks. impact feeding these patterns also briefly outlined, using 5 days as demonstrative case study. approach provide stakeholders with valuable information how their rearing environment small-scale settings used further development technologies measuring future studies.
Язык: Английский