PDF(Pubmed)
Abstract：
Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens\' bodies were cut out. Rectal temperature was used to label each infrared thermography data as \"Danger\" or \"Normal\", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.
摘要：
在最近的研究中，已经对红外热成像进行了研究，以监测体表温度并将其与动物福利和性能因素相关联。在这种情况下，这项研究提出了使用热签名方法作为从蛋鸡体表区域获得的温度矩阵的特征提取器(脸，眼睛,wattle,梳子,腿,和foot），以实现热应力水平分类的计算模型的构建。在气候控制室进行的实验中，192只产蛋鸡，34周大,来自两个不同菌株（DekalbWhite和DekalbBrown）的菌株被分为几组，并在热应激（35°C和60％湿度）和热舒适（26°C和60％湿度）的条件下饲养。每周,使用热成像相机收集母鸡的个体热图像，以及它们各自的直肠温度。切出母鸡身体的六个无羽图像区域的表面温度。直肠温度用于将每个红外热成像数据标记为“危险”或“正常”，和五种不同的分类器模型(随机森林，随机树,多层感知器,K-最近的邻居,和Logistic回归）使用各自的热特征生成直肠温度类别。在表面温度和直肠温度的热特征中没有观察到菌株之间的差异。事实证明，直肠温度和热信号表示热应力和舒适条件。蛋鸡面部面积的随机森林模型实现了最高的性能(89.0%)。对于wattle区，随机森林模型也展示了高性能(88.3%)，表明该区域在更发达的菌株中的重要性。这些发现验证了从红外热成像中提取特征的方法。当与机器学习相结合时，这种方法已被证明是有前途的生成分类器模型的热应力水平在蛋鸡生产环境。
