通过多标签分类来改善牛病检测
Ghalib Nadeem1, Muhammad Fahim Ul Haque2, Hameeza Ahmed3
1Department of Electrical and Computer Engineering, Iqra University, Karachi, 75500, Pakistan.
Scientific reports
|October 6, 2025
概括
这项研究引入了一种机器学习框架,用于使用传感器数据进行早期牛病检测. 该系统准确地识别了和乳腺炎等健康问题,改善了奶牛场动物的福利.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 农业技术 农业技术
- 数据科学数据科学数据科学
背景情况:
- 乳业生产率受到牛群健康问题 (,乳腺炎等) 的阻碍. ),影响牛奶产量和动物福利.
- 需要智能,数据驱动的监测系统来早期检测这些情况.
研究的目的:
- 提出基于机器学习 (ML) 的框架,用于使用多标签分类来早期检测牛健康事件和疾病.
- 通过行为指标识别诸如,分娩,,乳腺炎和酸性疾病等疾病.
主要方法:
- 利用机器学习框架对牛健康事件进行多标签分类.
- 从基于传感器的监测中分析了关键的行为指标 (食,休息,运动,活动).
- 采用SMOTE (合成少数人过量采样技术) 和分类器链来进行多标签预测,利用标签的相互依赖性.
主要成果:
- 在一个大数据集上进行了测试,其中包括235万个牲畜行为记录.
- 在分类器链配置中的额外树分类器实现了97%的子集精度,96%的回忆,95%的精度和96%的F1得分.
- 在标准的二进制相关性方法中表现出优异的性能,Hamming损失为0.04.
结论:
- 将分类器链与过量采样技术相结合,有效地捕获标签相关性,以改善牛病预测.
- 开发的ML框架为早期检测提供了一个有希望的解决方案,增强奶牛群的健康管理和动物福利.
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