Articlenpj veterinary sciences2026
Video-based cattle behaviour detection for digital twin development in precision dairy systems.
Article in npj veterinary sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Animal digital twins: systems architecture for climate-smart protein production.npj veterinary sciences · 2026Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital twins in dairy systems require reliable behavioural inputs. We develop a video-based framework that detects and tracks individual cows and classifies seven behaviours under commercial barn conditions. From 4964 annotated clips, expanded to 9600 through targeted augmentation, we couple YOLOv11 detection with ByteTrack for identity persistence and evaluate SlowFast versus TimeSformer for behaviour recognition. TimeSformer achieved 85.0% overall accuracy (macro-F1 0.84) and real-time throughput of 22.6 fps on NVIDIA L4 hardware. Attention visualizations concentrated on anatomically relevant regions (head and muzzle for feeding and drinking; torso and limbs for postures), supporting biological interpretability. Structured outputs (cow ID, start-end times, durations, and confidence) enable downstream use in nutritional modelling and integration with 3D digital-twin visualization environments, establishing a robust behavioural perception and state-estimation component within a dairy digital-twin architecture. The pipeline delivers continuous, per-animal activity streams suitable for individualized nutrition, predictive health, and automated management, providing a practical foundation for scalable dairy digital twins.
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Registered trials
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