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整合多模式数据融合方法来分析乳牛发声
Bubacarr Jobarteh1, Madalina Mincu-Iorga2, Dinu Gavojdian2
1Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Frontiers in veterinary science
|December 3, 2025
概括
我们开发了一个人工智能系统来分析乳牛的声音,以检测压力. 这项技术可以帮助监测动物福利,并在农业中及时进行健康干预.
科学领域:
- 动物行为 动物行为
- 人工智能的人工智能
- 精准畜牧业 精准畜牧业 精准畜牧业 精准畜牧业
背景情况:
- 对乳牛发声的非侵入性分析对于持续的压力评估和健康干预至关重要.
- 精密畜牧系统需要实用方法来实时监测动物福利.
研究的目的:
- 开发和验证一种多式人工智能框架,用于对乳牛发声进行分类,以评估压力水平.
- 要区分与唤醒相关的高频呼叫 (HFC) 和与平静状态相关的低频呼叫 (LFC).
主要方法:
- 声学特征 (频率,持续时间,振幅) 和基于变压器的表示被融合为行为分类.
- 使用支向量机和随机森林分类器,对哺乳期罗马尼亚霍尔斯坦犬的发声进行交叉验证.
- 一个标准化的社会隔离范式被用来引起声响应.
主要成果:
- 合并的声学和符号特征在分类呼叫类型时显著超过单模输入.
- 频率,大声和持续时间被确定为兴奋的主导,可解释的预测因素.
- 人工智能框架成功地区分了HFC和LFC,与压力和平静状态相关联.
结论:
- 开发的AI框架为实时福利监测和乳牛早期干预提供了可扩展的农场解决方案.
- 这个系统可以作为一个预警层,补充现有的传感器网络来检测健康和压力事件.
- 未来的工作将整合生理措施,以提高疾病特异性推断和概括性.
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