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使用热特征来评估产母的热应激水平,使用基于机器学习的分类器
Isaac Lembi Solis1, Fernanda Paes de Oliveira-Boreli2, Rafael Vieira de Sousa3
1Business Administration Undergraduate, School of Sciences and Engineering, São Paulo State University (UNESP), Tupã 17602-496, SP, Brazil.
Animals : an open access journal from MDPI
|July 13, 2024
概括
红外热学和机器学习可以准确地分类产母的热应激. 热特征方法,特别是来自脸部和部区域,在预测热应力水平方面表现出很高的表现.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 生物技术是生物技术.
背景情况:
- 动物的福利和表现受热应激等环境因素的影响.
- 红外热学提供了一种非侵入性方法来监测动物生理学.
- 开发精确的方法来检测热应激对于家禽生产至关重要.
研究的目的:
- 提出和验证用于从红外热图数据中提取特征的热特征方法.
- 构建计算模型来对产母的热应激水平进行分类.
- 为此分类任务评估不同机器学习模型的性能.
主要方法:
- 红外热学被用来捕捉来自产卵母在热应力和热舒适条件下的表面温度数据.
- 从特定的身体区域 (面部,眼睛,,,腿,脚) 提取热信号.
- 机器学习分类器 (随机森林,随机树,多层感知器,K-最近邻居,物流回归) 使用热签名和直肠温度标签进行训练.
主要成果:
- 在两个产卵母品种之间没有观察到热特征的显著差异.
- 直肠温度和热信号都有效地表明了热应力和热舒适度.
- 使用面积的随机森林模型实现了最高的分类性能 (89.0%),其次是瓦特尔区域 (88.3%).
结论:
- 热特征方法是从红外热学中提取特征的有效方法.
- 将红外热谱与机器学习相结合,为监测产母的热应激提供了一个有前途的工具.
- 特定的身体区域,如脸部和,是家禽热应激的重要指标.
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