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Updated: Jan 9, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

BECA: A Computer Vision Dataset for Long-Term Recognition In Beef Cattle.

Yuqi Zhang¹, Longxiang Li¹, Chunyang Li¹

¹Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China.

Scientific Data

|December 5, 2025

Summary

Researchers developed the Beef Cattle dataset (BECA) to address the lack of long-term cattle re-identification data. This dataset supports improved computer vision for precision livestock farming and cattle monitoring.

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Area of Science:

Computer Vision
Precision Livestock Farming
Animal Science

Background:

Computer vision in livestock farming faces challenges with long-term cattle re-identification due to significant phenotypic changes.
Existing datasets often lack the scale and duration needed for robust real-world applications.

Purpose of the Study:

To introduce the Beef Cattle dataset (BECA), a novel, large-scale resource for long-term and diverse cattle recognition.
To provide a benchmark for vision-based livestock management, including identification, behavior analysis, and welfare monitoring.

Main Methods:

BECA comprises two sub-datasets: BECA-D (16,889 images, 5,661 cattle) for visual diversity and BECA-L (12,172 images, 103 cattle) for long-term tracking (up to 5 months).
Includes annotation subsets for object detection and pose estimation to support diverse model development.

Main Results:

BECA offers a comprehensive dataset addressing the critical need for long-term cattle re-identification.
The dataset captures substantial phenotypic diversity and tracks individuals over extended periods.

Conclusions:

BECA establishes a valuable benchmark for advancing computer vision applications in cattle management.
Facilitates research in automated cattle identification, behavior analysis, and welfare monitoring in real-world farming conditions.