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

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.