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Related Experiment Video

Updated: Apr 21, 2026

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

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Published on: September 7, 2015

Video-based cattle behaviour detection for digital twin development in precision dairy systems.

Shreya Rao¹, Eduardo Garcia¹, Suresh Neethirajan^1,2

¹Faculty of Computer Science, Dalhousie University, Halifax, NS Canada.

Npj Veterinary Sciences

|April 20, 2026

Summary

We created a video system to track cows and identify seven behaviors for digital dairy farms. This technology provides data for better animal health and management.

Keywords:

Biological techniques Computational biology and bioinformatics Engineering Mathematics and computing Zoology

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Last Updated: Apr 21, 2026

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Area of Science:

Computer Vision
Animal Science
Digital Agriculture

Background:

Digital twins in dairy farming necessitate accurate behavioral data for effective modeling.
Current methods for monitoring cow behavior can be labor-intensive and lack real-time precision.

Purpose of the Study:

To develop and validate a video-based framework for detecting, tracking, and classifying individual cow behaviors in commercial barn settings.
To enable the integration of precise behavioral data into dairy digital twin architectures.

Main Methods:

Utilized YOLOv11 for cow detection and ByteTrack for identity tracking.
Employed the TimeSformer model for behavior recognition, trained on augmented video data.
Evaluated model performance using accuracy, macro-F1 score, and real-time throughput.

Main Results:

Achieved 85.0% overall accuracy and a macro-F1 score of 0.84 for behavior classification.
Real-time processing throughput of 22.6 fps was demonstrated on NVIDIA L4 hardware.
Attention visualizations confirmed focus on biologically relevant anatomical regions.

Conclusions:

The developed framework provides a robust behavioral perception and state-estimation component for dairy digital twins.
Continuous, per-animal activity streams facilitate individualized nutrition, predictive health, and automated farm management.
This system offers a practical foundation for scalable dairy digital twin applications.