ArticleScientific reports2022
Estimating genetics of body dimensions and activity levels in pigs using automated pose estimation.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Impact of Trait Measurement Error on Quantitative Genetic Analysis of Computer Vision-Derived Traits.Genes · 2026Article
- Artificial intelligence in animal breeding and genetics: applications, opportunities, and challenges.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Advancing precision livestock farming in pigs through markerless pose estimation: a comparison between DeepLabCut and SLEAP.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Genome-Wide Association Study Identifies Candidate Genes for Body Size Traits in Wanyue Black Pigs.Animals : an open access journal from MDPI · 2025Article
- "Why don't we just add a camera?": a psycho-genetic perspective on precision livestock farming in pigs.Porcine health management · 2024Article
- Deep learning pose detection model for sow locomotion.Scientific reports · 2024Article
- Breeding for resilience in finishing pigs can decrease tail biting, lameness and mortality.Genetics, selection, evolution : GSE · 2024Article
- A promising resilience parameter for breeding: the use of weight and feed trajectories in growing pigs.Journal of animal science and biotechnology · 2023Article
- BAMBI: A new method for automated assessment of bidirectional early-life interaction between maternal behavior and pup vocalization in mouse dam-pup dyads.Frontiers in behavioral neuroscience · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pig breeding is changing rapidly due to technological progress and socio-ecological factors. New precision livestock farming technologies such as computer vision systems are crucial for automated phenotyping on a large scale for novel traits, as pigs' robustness and behavior are gaining importance in breeding goals. However, individual identification, data processing and the availability of adequate (open source) software currently pose the main hurdles. The overall goal of this study was to expand pig weighing with automated measurements of body dimensions and activity levels using an automated video-analytic system: DeepLabCut. Furthermore, these data were coupled with pedigree information to estimate genetic parameters for breeding programs. We analyzed 7428 recordings over the fattening period of 1556 finishing pigs (Piétrain sire x crossbred dam) with two-week intervals between recordings on the same pig. We were able to accurately estimate relevant body parts with an average tracking error of 3.3 cm. Body metrics extracted from video images were highly heritable (61-74%) and significantly genetically correlated with average daily gain (r
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