Thermal stress in dairy cows has been studied to improve production efficiency and animal welfare. Several authors have verified the potential of infrared thermography (IRT) as noninvasive tool for monitoring the surface temperature of animals. In this study, machine learning-based models for the individual assessment of thermal stress levels in dairy cows (Holstein) were established and evaluated considering both weather and animal factors. An experiment was performed with 26 lactating cows, which were monitored during summer and winter (40 nonconsecutive days in total; three times a day) to acquire the weather and physiological data including the rectal temperature (RT), respiration rate (RR), and body surface temperature (BST), measured by IRT in different body area. The data were analyzed with the Pearson correlation coefficient to determine the ideal body part (front, ocular area, rib, and flank regions) for computational modeling. In addition, a data analysis with linear regression was performed to enhance the parameters choices. The models based on artificial neural networks (ANNs) were developed based on the defined weather and physiological variables. The ANN-based models for predicting the RR (ANN-RR) and RT (ANN-RT) were established with Perceptron, feedforward, and multi-layered architectures. The model responses were used as classifiers for the thermal stress levels (comfort, alert, danger, and emergency). The classification efficiency was assessed with metrics extracted from the confusion matrices (accuracy) and compared with the results of traditional classification methods for thermal stress levels: the Temperature–Humidity Index (THI) and Black Globe-Humidity Index (BGHI). The ANN models provided better predictions of the RR and RT with coefficient of determination (R²) of 0.74 (ANN-RR) and 0.71 (ANN-RT) than the linear regression models. With regard to thermal stress levels, the ANN-based models demonstrated a good predictive ability compared with the BGHI and THI classifications. The best thermal stress accuracy predictions with ANN-RR and ANN-RT were 83% and 84%, respectively; the best accuracies of the BGHI- and THI-based classifiers were 68% and 55%, respectively. In addition, the ANN-based classifier enables an individual assessment of the thermal stress levels of animals.

已经研究了奶牛的热应激以提高生产效率和动物福利。一些作者已经验证了红外热成像（IRT）作为监测动物表面温度的非侵入性工具的潜力。在这项研究中，建立了基于机器学习的模型来对奶牛（荷斯坦）的热应激水平进行单独评估，并考虑了天气和动物因素对其进行了评估。对26头泌乳母牛进行了实验，在夏季和冬季（总共40次非连续天；每天3次）进行监测，以获取天气和生理数据，包括直肠温度（RT），呼吸频率（RR）和体表温度（BST），通过IRT在不同的身体区域进行测量。使用Pearson相关系数分析数据，以确定用于计算建模的理想身体部位（前，眼部区域，肋骨和侧面区域）。另外，进行了具有线性回归的数据分析以增强参数选择。基于定义的天气和生理变量，开发了基于人工神经网络（ANN）的模型。使用Perceptron，前馈和多层体系结构建立了用于预测RR（ANN-RR）和RT（ANN-RT）的基于ANN的模型。模型响应用作热应力水平（舒适，警报，危险和紧急情况）的分类器。使用从混淆矩阵中提取的指标（准确性）评估分类效率，并将其与热应力水平的传统分类方法的结果进行比较：温度-湿度指数（THI）和黑地球仪-湿度指数（BGHI）。ANN模型通过确定系数（R²）比线性回归模型分别为0.74（ANN-RR）和0.71（ANN-RT）。关于热应力水平，与BGHI和THI分类相比，基于ANN的模型具有良好的预测能力。用ANN-RR和ANN-RT预测的最佳热应力精度分别为83％和84％。基于BGHI和THI的分类器的最佳准确性分别为68％和55％。此外，基于ANN的分类器可对动物的热应激水平进行单独评估。