针对猪的不同行为定制去噪方法，提升养殖监测准确率。
Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
- 按活跃与静止行为分别设计滤波策略
- 最高分类准确率达94.73%，优于传统方法的91.58%
- 适合需要精准动物行为识别的智慧养殖场
本研究提出一种行为特异性过滤方法，以提升精准畜牧养殖中的行为分类准确性。传统滤波方法（如小波去噪）虽达到91.58%的准确率，但对所有行为采用统一处理。本文提出的基于行为特异性的过滤方法，结合小波去噪与低通滤波，分别适配活跃与非活跃猪只行为，实现最高94.73%的分类准确率。结果表明，行为特异性过滤显著提升了动物行为监测效果，有助于改善健康管理和养殖效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。