Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
This work addresses the need for better animal health monitoring and farm efficiency in livestock farming, but it is incremental as it builds on existing filtering techniques.
The study tackled the problem of improving pig behavior classification accuracy in Precision Livestock Farming by proposing a behavior-specific filtering method, which achieved a peak accuracy of 94.73% compared to 91.58% with traditional methods.
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.