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Image dataset for cattle biometric detection and analysis.

Lili Bai1, Zhe Zhang1, Jie Song1

  • 1Software College, Northeastern University, Shenyang 110819, China.

Data in Brief
|September 12, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a valuable dataset of Horqin yellow cattle biometric features, crucial for advancing smart agriculture and automated livestock monitoring systems. The data supports enhanced animal welfare and efficient farm management through computer vision applications.

Keywords:
Automatic measurementBody measurementsIntelligent animal husbandryLivestock husbandry

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Animal Science

Background:

  • Improving livestock management is essential for smart agriculture.
  • Existing systems lack comprehensive biometric data for automated monitoring.
  • Horqin yellow cattle require specific data for breed-focused management.

Purpose of the Study:

  • To create a high-quality dataset of cattle biometric features.
  • To facilitate the development of deep learning models for livestock monitoring.
  • To support advancements in smart agriculture and animal husbandry.

Main Methods:

  • Collected images of 72 free-range Horqin yellow cattle (side and back views).
  • Acquired detailed annotations including body length, height, girth, hip length, and body weight.
  • Utilized mobile camera on grasslands for data acquisition.

Main Results:

  • A comprehensive dataset of cattle biometric images and annotations was established.
  • The dataset enables the construction of deep learning models for automated monitoring.
  • Facilitates body condition assessment and health monitoring research.

Conclusions:

  • The dataset is a pivotal asset for smart agriculture and livestock management innovation.
  • It supports the development of intelligent farm management systems with real-time alerts.
  • Drives modernization and digitization in animal husbandry for sustainable development.