优化机器学习模型用于预测乳牛的核心体温:提高实际畜牧管理的准确性和可解释性
Dapeng Li1,2, Geqi Yan3, Fuwei Li1,2
1Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Animals : an open access journal from MDPI
|September 28, 2024
概括
这项研究开发了一种机器学习模型,用于预测奶牛核心体温 (CBT) 和管理热应激. 灰狼优化器-极端梯度提升 (GWO-XGBoost) 模型准确地预测了使用红外干温度的CBT,改善了动物福利.
科学领域:
- 动物科学动物科学
- 机器学习 机器学习
- 环境生理学环境生理学
背景情况:
- 热应激显著影响奶牛的健康和生产力.
- 有效的管理策略对于动物福利和农场经济至关重要.
- 预测建模为主动缓解热应激提供了一种新的方法.
研究的目的:
- 开发和验证用于预测奶牛核心体温 (CBT) 的机器学习框架.
- 确定影响CBT的关键生理和环境因素.
- 优化预测模型性能,以提高热应激管理.
主要方法:
- 利用生产环境中的奶牛3005个生理记录的数据集.
- 应用了各种机器学习算法,包括弹性网,人工神经网络,随机森林,XGBoost,LightGBM和CatBoost.
- 使用贝叶斯优化和灰狼优化器进行超参数调整和模型改进.
主要成果:
- 包括平均红外干温度 (IRTave_TK) 在内的功能集显示出强大的预测能力 (R2=0.516,MAE=0.239°C,RMSE=0.302°C).
- GWO-XGBoost模型实现了最高的精度 (R2=0.540,RMSE=0.294°C,MAE=0.232°C) 和计算效率 (2.41s优化时间).
- SHAP分析确定IRTave_TK,时区 (TZ),哺乳期 (DOL) 和身体姿势 (BP) 作为CBT的关键预测因素.
结论:
- 机器学习模型,特别是GWO-XGBoost,可以准确预测奶牛的CBT.
- 红外热学和生理数据是热应激管理的宝贵投入.
- 该框架支持及时干预,以保持奶牛的健康和生产力.
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