Kroese, Adrien
- Department of Clinical Sciences, Swedish University of Agricultural Sciences
Abstract
This thesis sought to apply recent advances in precision livestock farming to evaluating a specific welfare parameter: cows’ comfort when getting up and lying down in cubicles. Cows need sufficient space to get up and lie down but rigid metal bars interfere with their innate motion patterns. A multi-camera system recorded over 12 cubicles during two data-collection phases. Triangulating 24 anatomical landmarks detected on each view using computer vision produced 3D pose estimation throughout posture transitions. From these, the timing, spatial use, and weight distribution could be measured or modelled. When compared with human annotations, the system showed high agreement in identifying rising and lying down events and their phases. While stage detection was not fully repeatable, the results show that 3D keypoint motion reliably reflects observable kinematic patterns. The method developed was then applied to evaluate whether replacing metal head and neck rails with flexible straps improved cows’ movement opportunities. An experiment was run in which the head and neck rails of cubicles were replaced with flexible straps. In the flexible setup, cows showed greater head vertical displacement and straighter lunge during rising, indicating greater movement opportunities. Effect sizes were small. Lying-down movements showed no consistent difference between flexible cubicles and both baselines. The duration of lying down movements increased upon returning to baseline, suggesting that duration alone doesn’t fully capture comfort. There was a consistent difference in a novel indicator introduced in this work: the shift of the cows’ centre of mass. The thesis concludes on the following: (i) Posture transition behaviours consist of multiple, independent dimensions and single-indicator assessments may not be a sound summarisation. (ii) Pose estimation in 3D represents a valuable technology to simultaneously monitor several indicators and uncover different strategies used to cope with restrictive environments. Finally (iii) that novel indicators such as modelled weight displacement are adapted to pose data and get a step closer to biomechanical drivers behind the specific motions and behaviours.
Keywords
precision livestock farming; animal welfare; free stall; rising behaviour; lying down behaviour; computer vision; dairy cattle; pose estimation
Published in
Acta Universitatis Agriculturae Sueciae
2026, number: 2026:6
Publisher: Swedish University of Agricultural Sciences
SLU Authors
UKÄ Subject classification
Clinical Science
Animal and Dairy Science
Computer graphics and computer vision
Publication identifier
- DOI: https://doi.org/10.54612/a.54p7dcvs0a
- ISBN: 978-91-8124-203-4
- eISBN: 978-91-8124-223-2
Permanent link to this page (URI)
https://res.slu.se/id/publ/144944