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

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SideCow-VSS: A Video Semantic Segmentation Dataset and Benchmark for Intelligent Monitoring of Dairy Cows Health in

Lei Yao¹, Jin Liu², Weinan Hong¹

¹College of Artificial Intelligence, Jilin University, Changchun 130012, China.

Veterinary Sciences

|November 26, 2025

Summary

A new dataset, SideCow-VSS, aids precision livestock farming by enabling AI-driven monitoring of dairy cows. This resource supports developing computer vision tools for early disease detection and improved herd health management.

Keywords:

computer vision dairy cows deep learning disease diagnosis precision livestock farming preventive strategies semantic segmentation

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Published on: August 31, 2018

Area of Science:

Computer Vision
Animal Science
Machine Learning

Background:

Precision livestock farming relies on non-invasive dairy cow monitoring for health management.
AI development is limited by a lack of realistic video datasets and standardized benchmarks for deep learning models.

Purpose of the Study:

Introduce SideCow-VSS, a novel video semantic segmentation dataset for dairy cows.
Evaluate deep learning model performance for automated cow monitoring tasks.
Provide a benchmark for future computer vision research in livestock farming.

Main Methods:

Collected 921 side-view video clips of dairy cows under various on-farm conditions.
Annotated data with dense, pixel-level segmentation masks.
Systematically evaluated eight deep learning architectures, including CNNs and Transformers.

Main Results:

Mask2Former with Swin-L backbone achieved the highest mIoU (97.32%) for morphological analysis.
PIDNet-s demonstrated the fastest inference speed (59.5 FPS) for real-time applications.
Performance varied across models, highlighting a trade-off between accuracy and speed.

Conclusions:

SideCow-VSS serves as a foundational resource for advancing computer vision in dairy farming.
The study provides a quantitative framework for selecting appropriate deep learning models.
This work facilitates the development of systems for automated health monitoring and disease prevention in dairy production.