We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.

中文翻译：

---

通过有监督的机器学习连续测量腹尾基面温度来检测牛的发情期

我们旨在通过对牛的监督机器学习连续测量腹尾基部表面温度 (ST) 来确定发情检测的有效性。ST 数据是通过 11 头雌性牛（6 头荷斯坦牛和 5 头日本黑牛）的 51 个发情周期使用尾连传感器获得的。基于从传感数据中提取的 13 个特征（指示与发情相关的 ST 变化），使用机器学习技术（随机森林、人工神经网络和支持向量机）使用训练数据（n = 17）构建了三个发情检测模型。三种模型对测试数据（n = 34）的发情检测能力在灵敏度和精确度方面在模型之间没有统计学差异（范围分别为50.0％至58.8％和60.6％至73.1％）。