Fogarty Eloise S, Swain David L, Cronin Greg M, Moraes Luis E, Bailey Derek W, Trotter Mark
Central Queensland Innovation and Research Precinct, Institute for Future Farming Systems, CQ University, Rockhampton, QLD 4701, Australia.
Faculty of Science-SOLES, The University of Sydney, Camden, NSW 2570, Australia.
Animals (Basel). 2021 Jan 25;11(2):303. doi: 10.3390/ani11020303.
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.
在当前的研究中，开发并报告了一种模拟在线分娩检测模型。该模型采用基于机器学习（ML）的方法，整合了来自全球导航卫星系统（GNSS）跟踪项圈、加速度计耳标和当地天气数据的数据，旨在检测以牧场为基础的绵羊的分娩事件。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，以便在分娩事件发生时发出警报。在新西兰2017年和2018年产羔季节进行了两项独立的田间试验，每项试验的数据分别用于ML训练和独立验证。基于目标（i），确定了四个对产羔检测最重要的特征：与同伴的平均距离（MDP）、与羊群平均距离相比的MDP（MDP.Mean）、最近的同伴（CP）和姿势变化（PC）。使用这四个特征，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，且无先前的误报。如果对模型灵敏度进行调整，使得允许更早的误报，则根据单次警报或连续两次警报的要求，这种检测率分别提高到91%和82%。为了确定模型失败的潜在原因，对三只动物的数据进行了进一步调查。产羔检测似乎除了依赖于增加的PC行为外，还依赖于增加的社会隔离行为。该研究结果支持使用综合传感器数据对放牧绵羊的分娩事件进行基于ML的检测。这是首次将ML分类应用于基于牧场的绵羊产羔检测。这种知识的应用可能会对商业环境中远程监测动物的能力产生重大影响，并合理扩展用于远程监测动物福利的信息。
