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在乳牛养殖中使用精选的机器学习方法:一篇评论
Wilhelm Grzesiak1, Daniel Zaborski1, Marcin Pluciński2
1Laboratory of Biostatistics, Bioinformatics and Animal Research, West Pomeranian University of Technology, 71-270 Szczecin, Poland.
Animals : an open access journal from MDPI
|July 29, 2025
概括
本综述强调了从2020-2024年开始在乳牛养殖中应用的机器学习 (ML) 算法. 它详细介绍了各种ML方法及其在育种和养殖中的使用,以及模型构建和性能评估.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 乳牛养殖越来越多地使用先进的计算方法.
- 机器学习 (ML) 为优化乳制品运营提供了强大的工具.
- 最近的进展需要对这一领域的ML应用进行审查.
研究的目的:
- 审查2020年至2024年间在乳牛养殖中使用的选定机器学习算法.
- 描述各种ML方法在乳牛繁殖和养殖中的应用.
- 概述ML模型的模型构建,实现和性能评估.
主要方法:
- 机器学习算法的审查,包括回归,树,随机森林,AdaBoost,SVM,k-NN,Naive Bayes,MARS,ANN (DNN,CNN),GMM和集群分析.
- 模型构建,实施阶段和绩效指标的描述.
- 分析ML方法在乳牛养殖中受欢迎的时间趋势.
主要成果:
- 选择的ML算法,如随机森林,ANN和SVM,在乳牛养殖中显示出重要的应用.
- 提供的例子涵盖了繁殖,健康和生产管理等多个领域.
- 对回归和分类模型的性能指标是详细的,有助于方法选择.
结论:
- 机器学习是现代奶牛养殖中快速发展和越来越重要的工具.
- 了解不同的ML算法及其应用对于有效的农场管理至关重要.
- 这一趋势表明,在优化乳制品生产和动物福利方面,ML的持续增长和整合.
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