使用数据挖掘和机器学习算法,通过生长毛发羊的生物识别测量来预测体重
Ignacio Vázquez-Martínez1,2, Cem Tırınk3, Rosario Salazar-Cuytun1
1División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, km 25, Carretera Villahermosa-Teapa, R/A La Huasteca, 86280, Villahermosa, Tabasco, Mexico.
Tropical animal health and production
|September 21, 2023
概括
准确的活体重预测对于可持续的牲畜管理至关重要. 这项研究发现,支持向量机器回归 (SVR) 能够有效地利用身体测量预测毛羊的体重,为群体管理提供了有价值的工具.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 数据科学数据科学数据科学
背景情况:
- 准确的活体重测定对于有效的畜牧管理和可持续的畜牧生产至关重要.
- 传统的称重方法在农村环境中可能不切实际,需要采用替代方法.
- 身体测量为羊的活体重估计提供了潜在的代理.
研究的目的:
- 评估数据挖掘和机器学习算法的有效性,用于使用身体测量来预测毛羊的活体重.
- 为了比较多变量自适应回归线 (MARS),分类和回归树 (CART) 和支持矢量机回归 (SVR) 算法的性能.
- 确定一种可靠的方法来估计毛羊的活体重,特别是在没有权重工具的情况下.
主要方法:
- 利用了280只毛羊 (2个月到3岁) 的身体测量数据.
- 应用机器学习算法:MARS,CART和SVR,70%用于培训,30%用于测试.
- 使用R2和r值等适用性标准评估算法性能.
主要成果:
- 无论是MARS还是SVR算法都表现出高性能,在训练和测试数据集上,R2和r值的最高结果相似.
- 特别推使用SVR算法,因为它在各种适合度指标上表现强.
- 这项研究成功地在毛羊品种的体型测量和活体重之间建立了关系.
结论:
- 支持矢量机器回归 (SVR) 是一个非常有效的机器学习工具,用于预测毛羊的活重.
- 这种方法可以帮助建立品种标准,并有可能提高墨西哥绵羊肉的质量.
- 这些发现支持机器学习在资源有限的环境中用于实际的牲畜管理.
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