Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming

Fuente: arXiv
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Auteurs principaux: Zhang, Zhen, Ha, Dong Sam, Morota, Gota, Shin, Sook
Format: Preprint
Publié: 2025
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author Zhang, Zhen
Ha, Dong Sam
Morota, Gota
Shin, Sook
author_facet Zhang, Zhen
Ha, Dong Sam
Morota, Gota
Shin, Sook
contents This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming. While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behavior-specific filtering method combines Wavelet Denoising with a Low Pass Filter, tailored to active and inactive pig behaviors, and achieved a peak accuracy of 94.73%. These results highlight the effectiveness of behavior-specific filtering in enhancing animal behavior monitoring, supporting better health management and farm efficiency.
format Preprint
id arxiv_https___arxiv_org_abs_2507_21021
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
Zhang, Zhen
Ha, Dong Sam
Morota, Gota
Shin, Sook
Machine Learning
This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming. While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behavior-specific filtering method combines Wavelet Denoising with a Low Pass Filter, tailored to active and inactive pig behaviors, and achieved a peak accuracy of 94.73%. These results highlight the effectiveness of behavior-specific filtering in enhancing animal behavior monitoring, supporting better health management and farm efficiency.
title Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
topic Machine Learning
url https://arxiv.org/abs/2507.21021