Zhen Zhang ¹, Dong Sam Ha ¹, Gota Morota ^1,2 and Sook Shin¹, ¹ Virginia Tech, USA, ² The University of Tokyo, Japan

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.

Precision Livestock Farming, Behavior-Specific Filtering, Behavior Classification, Sensor Data