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Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

Hassan Seif Mluba¹, Othmane Atif¹, Jonguk Lee²

¹Department of Computer and Information Science, Korea University, Sejong City 30019, Republic of Korea.

Sensors (Basel, Switzerland)

|April 13, 2024

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This study introduces a pig behavior analysis system using pattern mining to help farmers monitor pig health and welfare. The system effectively recognizes behaviors, aiding in better farm management despite labor shortages.

Area of Science:

Agricultural Technology
Animal Behavior Science
Computer Vision

Background:

Increasing pig production faces labor shortages, impacting animal health and welfare monitoring.
Effective management of pig health and welfare is crucial for the growing swine industry.

Purpose of the Study:

To develop and evaluate a pattern mining-based system for pig behavior analysis.
To assist farmers in monitoring and assessing pig health and welfare through visualized data and behavioral patterns.

Main Methods:

A four-module system was developed: data acquisition, pig detection/tracking (ByteTrack with YOLOx and BYTE), behavior recognition (MnasNet and LSTM), and behavior analysis.
Utilized ByteTrack for precise pig localization and identification, and MnasNet/LSTM for recognizing behaviors from video sequences.

Keywords:

association rule mining data visualization pig behavior analysis pig behavior recognition pig health and welfare sequential pattern mining

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The system achieved high accuracy in multi-object tracking (0.971) and behavior recognition (F1 score of 0.931).
Demonstrated the effectiveness of visualization and pattern mining in enhancing farmer comprehension of pig welfare.

Conclusions:

The proposed system offers a viable solution for automated pig health and welfare monitoring.
Pattern mining and visualization effectively support farmers in managing pig well-being amidst production challenges.