使用随机森林回归,支向量回归以及分类和回归树算法预测土著绵羊的体重
Ashenafi Getachew Megersa1, Fikrineh Negash2, Abebe Hailu3
1School of Animal and Range Sciences, Haramaya University, P. O. Box 138, Dire Dawa, Ethiopia.
Veterinary and animal science
|October 20, 2025
概括
随机森林回归 (RFR) 准确地预测了土著埃塞俄比亚羊的体重,使用像心脏周长这样的形态特征. 这种方法为缺乏秤的农民提供了切实可行的解决方案,有助于繁殖计划.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 机器学习 机器学习
背景情况:
- 准确的体重 (BW) 估计对于畜牧管理和繁殖计划至关重要.
- 在资源有限的环境中,使用秤的传统BW测量可能是不切实际的.
- 形态特征为BW预测提供了一个非侵入性的替代方案.
研究的目的:
- 开发和比较预测模型来估计土著埃塞俄比亚绵羊的体重.
- 评估随机森林回归 (RFR),支持矢量回归 (SVR) 和分类和回归树 (CART) 算法的性能.
- 为了确定关键的形态学特征,以准确地预测BW.
主要方法:
- 收集了306只本土绵羊 (母羊和公羊) 的BW和16个线性身体测量 (LBM) 数据.
- 应用RFR,SVR和CART算法来构建预测模型.
- 使用R平方 (R2) 和根平均平方误差 (RMSE) 评估模型性能.
- 进行了变量重要性分析,以确定关键预测因素.
主要成果:
- RFR表现出卓越的预测准确性,具有最高的R2 (0.809培训,0.477验证) 和最低的RMSE (1.650培训,2.825验证).
- 心脏周长 (HG) 是所有模型中最有影响力的预测指标,其次是身体长度 (BL) 和身高 (HAW).
- SVR显示了良好的训练表现,但普遍性较低,而CART表现最弱.
结论:
- RFR是一种强大而准确的工具,用于使用形态特征预测羊的体重,特别是在资源有限的环境中.
- 心脏周长是一个高度可靠的指标,用于估计土著埃塞俄比亚绵羊的BW.
- 该研究为小型农民和养殖计划提供了宝贵的见解,以改善牲畜管理和遗传选择.
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