利用无监督的机器学习技术来检测乳牛的每日奶产数据中的异常值
Shogo Higaki1, Eduardo Noronha de Andrade Freitas2, Ariana Negreiro3
1National Institute of Animal Health, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-0856, Japan; Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
Journal of dairy science
|July 17, 2025
概括
无监督的机器学习模型有效地检测出牛奶产量下降,并估计奶牛的哺乳潜力. 这些方法提高了对传统模型的准确性,以更好地管理奶牛场.
科学领域:
- 乳制品科学 乳制品科学
- 机器学习 机器学习
- 动物生产 动物生产
背景情况:
- 准确的哺乳曲线建模对于奶牛场管理至关重要.
- 健康障碍和环境因素会引起干扰,使得产量估计变得复杂.
- 现有的模型很难准确地识别和调整这些扰动.
研究的目的:
- 评估无监督的机器学习模型,用于在每日牛奶产量数据中检测异常值.
- 评估这些模型估计未扰乱哺乳曲线 (ULC) 的能力.
- 将无监督机器学习模型的性能与现有方法进行比较.
主要方法:
- 使用了三个无监督机器学习模型 (UMLM):一类SVM,隔离森林和局部异常因素.
- 将UMLM衍生的ULC与基线Wood模型,扰动乳房模型 (PLM) 和代Wood模型 (IWM) 进行比较.
- 进行模拟研究,并将模型应用于来自霍尔斯坦牛的真实世界牛奶产量数据.
主要成果:
- 与木材 (64.2%),PLM (53.2%) 和IWM (66.8%) 相比,UMLM表现出更好的干扰检测 (F1得分~70%).
- UMLM建立了比PLM和IWM更好的适合性和计算效率的ULC.
- UMLM发现了其他模型遗漏的潜在干扰,特别是在早期哺乳期.
结论:
- 无监督的机器学习技术有效地检测异常值,并估计奶牛的未经干扰的哺乳曲线.
- UMLM提供了一个有前途的方法,可以更准确地评估哺乳潜力,并改进乳制品管理策略.
- 未来的研究应该纳入多个农场的数据,并考虑固定效应,以提高模型的通用性.
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