使用随机森林的比利时蓝色杂交品种的体重预测
Lisa Praharani1, Chalid Talib1, Diana Andrianita Kusumaningrum1
1Research Center for Animal Husbandry, Research Organization for Agriculture and Food, National Research Innovation Agency of the Republic of Indonesia, Bogor, Indonesia.
Journal of advanced veterinary and animal research
|April 29, 2024
概括
预测比利时蓝色X弗里西亚霍尔斯坦杂交品种的体重 (BW) 是可以使用形态测量. 胸围 (CG) 是一个关键指标,随机森林模型的表现优于传统的回归,用于准确的BW估计.
科学领域:
- 动物科学动物科学
- 农业技术 农业技术
- 机器学习在农业中的应用
背景情况:
- 准确的体重 (BW) 预测对于畜牧管理和育种计划至关重要.
- 形态测量测量为评估动物生长和状况提供了一种非侵入性方法.
研究的目的:
- 开发和比较比利时蓝色X弗里西亚霍尔斯坦 (BB X FH) 杂交牛在印度尼西亚的体重 (BW) 的预测模型.
- 评估形态测量测量的有效性,特别是胸围 (CG),用于估计BW.
主要方法:
- 采用了26只BB X FH杂交品种的形态测量数据 (体重,胸部重量,身体长度,部高度,部高度,胸围).
- 应用阶段回归和随机森林算法用于预测建模.
- 数据分析使用R版本3.6.1.1.进行.
主要成果:
- 随机森林分析发现胸围 (CG) 是BW估计的一个非常重要的变量,贡献了24.49%.
- 无论是逐步回归还是随机森林模型,都表明CG是重要的选择指标.
- 随机森林模型实现了较高的R平方值 (0.86),而不是逐步回归 (0.83).
结论:
- 随机森林模型提供了比步骤回归更准确的BB X FH杂交动物的BW预测.
- 胸围 (CG) 是可靠且简单的形态参数,用于估计这种杂交种群的体重.
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