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Pig Health Assessment Framework Based on Behavioural Analysis.

Shuqin Tu1, Boyang Tan1, Aqing Yang2

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

Animals : an Open Access Journal From MDPI
|December 30, 2025
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Summary
This summary is machine-generated.

This study introduces an automated pig health assessment framework using multi-object tracking to analyze pig behavior. The system accurately monitors pig health, improving intelligent management in modern farming.

Keywords:
ByteTrackhealth assessmentmulti-object trackingpig

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Area of Science:

  • Animal Science
  • Computer Vision
  • Agricultural Technology

Background:

  • Accurate pig behavior analysis is crucial for modern farming but manual methods are often inaccurate.
  • Existing systems lack the precision needed for large-scale, intelligent pig management.

Purpose of the Study:

  • To develop an automated framework for pig health assessment using advanced behavior tracking.
  • To improve the accuracy and efficiency of pig health monitoring in large-scale farming environments.

Main Methods:

  • Implemented an improved ByteTrack algorithm for multi-object tracking (MOT) of pigs.
  • Developed behavior statistics and analysis modules integrated with a health assessment module.
  • Utilized two datasets of healthy and unhealthy pigs for validation.

Main Results:

  • The improved ByteTrack algorithm achieved high MOT performance (HOTA: 74.0%, MOTA: 92.2%, IDF1: 89.4%).
  • Behavioral data enabled reliable health assessment, accurately evaluating individual pig health status.
  • The framework demonstrated effectiveness in pig health monitoring.

Conclusions:

  • The proposed framework offers a robust solution for automated pig health monitoring.
  • This technology provides reliable technical support for intelligent management in modern pig farming.
  • The study highlights the potential of computer vision and AI in animal husbandry.