Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock.
Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock.
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DOI：
10.3390/s21010088
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发表时间：
2020-12-25
期刊：
影响因子：
--
通讯作者：
Kaler J
中科院分区：
文献类型：
--
作者：
Carslake C;Vázquez-Diosdado JA;Kaler J
先前的研究表明，监测说谎行为和喂养的传感器可以发现小牛健康状况不佳的早期迹象。有证据表明，监测单一行为的变化可能不足以预测疾病。对小牛来说，运动游戏、自我梳理、进食和躺着时的活动等多种行为可能会提供信息。然而，这些行为在现实世界中很少发生，这意味着简单地基于分类器的预测来计算行为可能会导致高估。在这里，我们为13头断奶前的奶牛配备了项圈上的传感器，并用摄像机监控它们的行为。行为观察被记录下来并与传感器信号合并。计算1-10-s窗口的特征，并使用AdaBoost集成学习算法对行为进行分类。最后，我们开发了一种调整计数量化算法来预测运动游戏行为在低真实患病率（0.27%）的测试数据集中的患病率。我们的算法识别了运动游戏（准确率99.73%）、自我梳理（准确率98.18%）、反刍（准确率94.47%）、非营养性哺乳（准确率94.96%）、营养性哺乳（准确率96.44%）、主动躺下（准确率90.38%）和非主动躺下（准确率90.38%）。我们的结果详细介绍了推荐的采样频率、特征选择和窗口大小。运动游戏行为的量化估计值与真实患病率高度相关（0.97;p < 0.001），总高估率为18.97%。这项研究首次将机器学习方法应用于小牛的多类行为识别和行为量化。这有可能有助于通过使用可穿戴传感器来评估小牛的健康和福利。
Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In calves, multiple behaviours such as locomotor play, self-grooming, feeding and activity whilst lying are likely to be informative. However, these behaviours can occur rarely in the real world, which means simply counting behaviours based on the prediction of a classifier can lead to overestimation. Here, we equipped thirteen pre-weaned dairy calves with collar-mounted sensors and monitored their behaviour with video cameras. Behavioural observations were recorded and merged with sensor signals. Features were calculated for 1–10-s windows and an AdaBoost ensemble learning algorithm implemented to classify behaviours. Finally, we developed an adjusted count quantification algorithm to predict the prevalence of locomotor play behaviour on a test dataset with low true prevalence (0.27%). Our algorithm identified locomotor play (99.73% accuracy), self-grooming (98.18% accuracy), ruminating (94.47% accuracy), non-nutritive suckling (94.96% accuracy), nutritive suckling (96.44% accuracy), active lying (90.38% accuracy) and non-active lying (90.38% accuracy). Our results detail recommended sampling frequencies, feature selection and window size. The quantification estimates of locomotor play behaviour were highly correlated with the true prevalence (0.97; p < 0.001) with a total overestimation of 18.97%. This study is the first to implement machine learning approaches for multi-class behaviour identification as well as behaviour quantification in calves. This has potential to contribute towards new insights to evaluate the health and welfare in calves by use of wearable sensors.
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