机器学习算法的应用来描述奶牛羊的特征 哺乳曲线
Lilian Guevara1, Félix Castro-Espinoza2, Alberto Magno Fernandes1
1Centro de Ciências e Tecnologias Agropecuárias, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes 28013-620, Brazil.
Animals : an open access journal from MDPI
|September 9, 2023
概括
机器学习算法有效地预测羊的哺乳曲线参数. 与传统模型相比,SMOreg表现出更高的准确性,提供了有价值的农场层面决策见解.
科学领域:
- 动物科学动物科学
- 数据科学数据科学数据科学
- 农业建模 农业建模
背景情况:
- 机器学习 (ML) 越来越多地用于复杂的数据建模.
- 准确预测哺乳曲线对于乳制品管理至关重要.
- 传统的经验模型可能在预测哺乳期参数方面存在局限性.
研究的目的:
- 将各种ML算法的预测性能与传统的实证模型 (伍德模型) 进行比较.
- 使用ML估计绵羊哺乳曲线的关键参数.
- 以有限的数据来评估ML适用于预测哺乳曲线特征的适用性.
主要方法:
- 利用来自156只母乳养羊的1186个月记录.
- 训练并测试了多个ML算法.
- 用伍德模型进行比较,拟合了哺乳曲线.
- 使用相关系数 (r),MAE,RMSE,RAE和RRSE评估模型性能.
主要成果:
- 该SMOreg ML算法实现了最好的预测准确性为绵羊哺乳曲线参数.
- 与伍德模型 (r=0.68) 相比,SMOreg对总牛奶产量的相关系数显著更高 (r=0.96).
- 机器学习算法表现出足够的预测能力,即使在有限数量的输入数据点.
结论:
- 机器学习算法,特别是SMOreg,对于预测绵羊哺乳曲线参数非常有效.
- 机器学习模型为传统方法提供了可行的替代方案,可以用更少的数据提供准确的估计.
- 可解释的ML算法可以支持农场层面的决策,以优化资源管理.
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