Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
Zhen Zhang (1), Dong Sam Ha (1), Gota Morota (2,3), and Sook Shin (1)((1) The Bradley Department of Electrical, Computer Engineering, Virginia Tech, Blacksburg, Virginia, USA, (2) Department of Animal, Poultry Sciences, Virginia Tech, Blacksburg, Virginia, USA

TL;DR
This paper introduces a behavior-specific filtering approach combining wavelet denoising and low pass filtering to improve pig behavior classification accuracy in precision livestock farming, achieving a peak accuracy of 94.73%.
Contribution
It presents a novel filtering method tailored to specific pig behaviors, outperforming traditional uniform filtering techniques.
Findings
Traditional filtering achieved 91.58% accuracy.
Behavior-specific filtering reached 94.73% accuracy.
Enhanced behavior classification supports better farm management.
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.
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