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New BoviShift Dataset Audits Visual Cattle Phenotyping Over Six Years

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced BoviShift, a new dataset designed to address challenges in visual cattle phenotyping. This dataset, collected over six years from 2021 to 2026, includes over 2,600 cattle with more than 10,000 images and 2,500 videos. Evaluations using a ResNet-50 baseline revealed that common image-random splitting can lead to data leakage, and model performance varies significantly when applied to future cohorts, particularly for attributes like breed, weight, and age. The study also highlighted the benefits of multi-task training and aggregation strategies, while exposing a domain gap between still images and video frames. AI

IMPACT This dataset could improve the robustness and transferability of AI models used in livestock management and agricultural monitoring.

RANK_REASON The cluster contains a research paper detailing a new dataset and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New BoviShift Dataset Audits Visual Cattle Phenotyping Over Six Years

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The cluster contains a research paper detailing a new dataset and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Mobasshir Bhuiya Shagor, Nilambar Halder Tonmoy, Md Saef Ullah Miah ·

    BoviShift: A Six-Year Multi-Attribute Audit of Visual Cattle Phenotyping Across Animals and Time

    arXiv:2611.14476v1 Announce Type: new Abstract: Computer vision offers a scalable, non-contact means of estimating livestock traits, but correlated images and video frames make reliable evaluation difficult. Image-random splitting can place views of the same animal in both traini…

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