大数据对牛生物声学的方法:一个符合FAIR的数据集和可扩展的ML框架,用于精确的牲畜福利
Mayuri Kate1, Suresh Neethirajan1,2
1Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Frontiers in big data
|February 2, 2026
概括
这项研究引入了用于精密畜牧业的大型,符合FAIR标准的牛声声数据集. 它使先进的人工智能能够使用生物声学数据进行非侵入性动物福利监测.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 动物行为 动物行为
背景情况:
- 精密畜牧利用物联网,边缘计算和机器学习.
- 由于计算和生态挑战,牲畜中的生物声学数据未得到充分利用.
- 现有的数据集缺乏适用于现实世界的应用的规模和生态有效性.
研究的目的:
- 创建一个全面的,符合FAIR的牛声声数据集.
- 为生物声学分析开发一个强大的数据处理工作流.
- 为了实现可扩展的,非侵入性的乳制品农场动物福利监测.
主要方法:
- 收集并策划了来自三个商业奶牛农场的48个行为类的569个牛声声片段.
- 将数据集扩展到2900个样本,使用域信息化数据增强.
- 开发了一个模块化工作流程,包括无声化,音频视频同步,并使用Praat,librosa和openSMILE进行功能工程.
主要成果:
- 该数据集解决了大数据挑战 (体积,种类,速度,真实性).
- 初步的机器学习模型识别了,危险和母亲沟通的独特声学特征.
- 数据集的生态现实主义支持部署准备的模型开发.
结论:
- 这项工作为畜牧业中以动物为中心的AI提供了基础.
- 生物声学数据集和开源管道促进可重现的研究和福利优化.
- 该框架支持联合国可持续发展目标9,将农业转化为智能,福利优化的系统.
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