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Related Experiment Video

Updated: May 28, 2026

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
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Airborne Intelligent System for Abnormal Pig Behavior Identification and Locking.

Yun Wang1, Haopu Li1, Zhihui Xiong2

  • 1College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Animals : an Open Access Journal From MDPI
|May 27, 2026
PubMed
Summary

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This study introduces an automated pig health monitoring system using advanced tracking and anomaly detection. The system significantly improves real-time surveillance in intensive farming, enhancing early disease detection and animal welfare.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Animal Science

Background:

  • Intensive pig farming faces challenges in individual health monitoring due to high density and complex environments.
  • Manual inspection is labor-intensive and leads to delayed detection of health issues.
  • Need for automated, real-time surveillance systems for improved animal welfare and disease management.

Purpose of the Study:

  • To develop an embedded intelligent monitoring system for automated pig health surveillance.
  • To improve multi-object tracking and anomaly detection in challenging farming conditions.
  • To enhance the efficiency and accuracy of health monitoring in intensive livestock farming.

Main Methods:

  • Integration of a pan-tilt gimbal with an improved Periodfill_DeepSORT algorithm for multi-object tracking.
Keywords:
aerial platformdeep learningembedded systemidentity recognitiontarget locking

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  • Utilized a ReID network incorporating appearance features and motion prediction for robust tracking under occlusion.
  • Developed a lightweight YOLOv8-based network for anomaly detection, trained on diverse abnormal behaviors.

    • Main Results:

    • Periodfill_DeepSORT achieved high tracking accuracy (MOTA 95.34%, MOTP 94.77%, IDF1 96.88%) with reduced identity switches.
    • Tracking accuracy significantly improved in occlusion scenarios (MOTA from 61.1% to 78.3%).
    • Anomaly detection network achieved 94.5% overall accuracy, with specific high accuracies for movement, postural, and disease-related abnormalities.

    Conclusions:

    • The developed embedded intelligent monitoring system is effective for automated pig health surveillance.
    • The improved tracking and anomaly detection algorithms enhance real-time monitoring capabilities in intensive farming.
    • The system demonstrates practical feasibility for improving animal welfare and disease management in livestock environments.