Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Zhang, Zhen, Ha, Dong Sam, Morota, Gota, Shin, Sook
Institutions: Virginia Tech, The University of Tokyo
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7077048247
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