使用三轴加速计对传统和缓慢生长的肉菌株的行为进行分类
Justine Pearce1, Yu-Mei Chang1, Dong Xia1
1The Royal Veterinary College, Hawkshead Lane, Brookmans Park, Hatfield AL9 7TA, UK.
Animals : an open access journal from MDPI
|July 13, 2024
概括
使用加速度计和随机森林算法自动监测肉的行为,准确识别行走,坐着和站着. 这项技术增强了福利评估,并减少了手动观察的限制.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 机器学习 机器学习
背景情况:
- 评估肉福利,包括,依赖于观察行走,坐着和站着等行为.
- 手动行为观察是耗时的,并且受到小样本大小的限制.
- 三维加速度计为收集广泛的动物行为数据提供了潜在的解决方案.
研究的目的:
- 开发和验证一个随机森林算法,使用加速仪数据自动分类肉的行为.
- 为了评估算法的性能跨不同的肉品种和年龄,包括一个看不见的菌株.
主要方法:
- 应用一个随机森林算法来处理来自肉的加速度计数据.
- 训练并测试了使用三种肉品种 (年龄2549天) 的数据的算法.
- 在一个独立的,看不见的肉品种上验证了算法.
主要成果:
- 随机森林模型在已知的菌株上进行测试时,获得了高精度 (92%) 和特异性 (94%).
- 该模型表现出非常好的准确性 (94%) 和高灵敏度 (91%) 在一个看不见的肉品种.
- 该算法成功地分类了四个菌株和不同年龄段的行走,坐着和站着的行为.
结论:
- 一个随机森林模型有效地使用加速度计数据自动检测关键肉行为.
- 这种自动化方法可以补充生物力学和行为研究,支持动物研究中的3R原则.
- 基于加速器的行为监测提供了一个可扩展和准确的方法来评估肉福利.
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