In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

中文翻译：

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开发一个集成了GNSS，加速度计和天气数据的模拟在线模型，以检测放牧绵羊的分娩事件：一种机器学习方法

在当前的研究中，开发并报告了模拟的在线分娩检测模型。该模型使用基于机器学习（ML）的方法，合并了来自全球导航卫星系统（GNSS）跟踪项圈，加速度计耳标和本地天气数据的数据，目的是检测基于牧场的绵羊的分娩事件。具体目标有两个：（i）确定哪些传感器系统和特征为产羔检测提供最有用的信息；（ii）评估如何使用ML分类来整合这些数据，以在发生分娩事件时发出警报。在2017年和2018年的羔羊季节期间，在新西兰进行了两次独立的田间试验，每个试验的数据分别用于ML训练和独立验证。根据目标（i），确定了对产羔检测最重要的四个特征：平均到同龄人的距离（MDP），与鸡群均值相比的MDP（MDP.Mean），最近的同伴（CP）和姿势变化（PC）。使用这四个功能，最终的ML能够在出生后±3小时内检测到27％和55％的产羔事件，而没有事先的假阳性。如果对模型敏感度进行了控制，以允许较早的假阳性，则根据单个警报或两个连续警报发生的要求，此检测会增加到91％和82％。为了确定模型失效的潜在原因，进一步研究了三只动物的数据。羔羊检测似乎除了增加PC行为外，还依赖于增加的社会隔离行为。研究结果支持使用集成的传感器数据基于ML的放牧绵羊分娩事件检测。这是ML分类法在基于牧场的绵羊中检测羔羊的第一个已知应用。应用此知识可能会对在商业情况下远程监视动物的能力产生重大影响，同时对信息进行逻辑扩展以对动物福利进行远程监视。