Video-Based Cattle Behavior Detection for Digital Twin Development in Precision Dairy Systems
Bibliographic record
Abstract
Abstract Digital twins in dairy systems require reliable behavioral inputs. We develop a video-based framework that detects and tracks individual cows and classifies seven behaviors under commercial barn conditions. From 4,964 annotated clips, expanded to 9,600 through targeted augmentation, we couple YOLOv11 detection with ByteTrack for identity persistence and evaluate SlowFast versus TimeSformer for behavior recognition. TimeSformer achieved 85.0% overall accuracy (macro-F1 0.84) and real-time throughput of 22.6 fps on RTX A100 hardware. Attention visualizations concentrated on anatomically relevant regions (head/muzzle for feeding and drinking; torso/limbs for postures), supporting biological interpretability. Structured outputs (cow ID, start-end times, durations, confidence) enable downstream use in nutritional modeling and 3D digital-twin visualization. The pipeline delivers continuous, per-animal activity streams suitable for individualized nutrition, predictive health, and automated management, providing a practical behavioral layer for scalable dairy digital twins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".