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Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor

机译:使用惯性测量单元传感器对不同草坪表面高度牧草放牧行为的比较

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Grazing is the most important activity that ruminant livestock undertake daily. A number of studies have used motion sensors to study the grazing behaviour of ruminant livestock. However, few have attempted to validate their approaches against various sward surface heights (SSH). The objectives of our study were to: (1) identify and compare the effects of different SSH on the grazing behaviour of sheep by analyzing data collected by a collar mounted Inertial Measurement Unit (IMU) sensor; (2) calculate the relative importance of the extracted features on grazing identification and compare the consistency of the selected features across various SSH; (3) validate the robustness by using classifiers trained from the dataset with specific SSH to distinguish the grazing activity on the datasets from different SSH; and (4) visualize the classification results of grazing versus nongrazing activities on various SSH. Linear Discriminant Analysis (LDA) was chosen as the classification method, while Probabilistic Principal Component Analysis (PPCA) was used to reduce dimensionality of the feature space for visualization of the results. Experimental results revealed that (1) our approach achieved high classification accuracy of grazing behaviour (over 95%) on all the epochs regardless of SSH; (2) Mean of accelerometer Z-axis, Entropy of accelerometer Y-axis, Entropy of accelerometer Z-axis, Mean of gyroscope X-axis and Mean of gyroscope Y-axis were the top 5 features that contributed most in classifying the grazing versus non-grazing activities and there were consistent trends in features across the three SSH; (3) there was enough robustness when the trained LDA classifier on a specific SSH was used to classify behaviour on different SSH; and (4) there existed a clear linear boundary between the data points representing grazing and those of non-grazing behaviour. Overall, our research confirmed that IMU sensors can be a very effective tool for identifying the grazing behaviour of sheep and there is enough robustness to use a trained LDA classifier on a specific pasture SSH to classify grazing behaviour at different SSH pastures.
机译:放牧是反刍动物牲畜每天进行的最重要的活动。许多研究使用了运动传感器来研究反刍动物牲畜的放牧行为。然而,很少有人试图验证其针对各种草地表面高度(SSH)的方法。我们研究的目标是:(1)通过分析由套环安装惯性测量单元(IMU)传感器收集的数据来识别不同SSH对绵羊放牧行为的影响; (2)计算提取的功能对放牧识别的相对重要性,并比较各种SSH中所选功能的一致性; (3)使用具有特定SSH的数据集培训的分类器来验证鲁棒性,以将放牧活动区分开于不同SSH的数据集; (4)在各种SSH上可视化放牧与非崇高活动的分类结果。选择线性判别分析(LDA)作为分类方法,而概率主成分分析(PPCA)用于减少特征空间的维度以便可视化结果。实验结果表明,(1)我们的方法在所有时代的放牧行为(超过95%以上)达到了高分性的准确性,无论SSH如何; (2)加速度计Z轴的平均值,加速度计y轴的熵,加速度计Z轴熵,陀螺仪X轴的平均值和陀螺仪Y轴的平均值是最重要的,在分类放牧方面贡献的前5个功能。非放牧活动和三个SSH的特征趋势一致; (3)当特定SSH上的训练有素的LDA分类器用于对不同SSH进行分类行为时,有足够的稳健性; (4)在代表放牧和非放牧行为的数据点之间存在明显的线性边界。总体而言,我们的研究证实,IMU传感器可以是识别绵羊放牧行为的非常有效的工具,并且在特定牧场SSH上使用训练有素的LDA分类器有足够的稳健性来对不同的SSH牧场进行分类放牧行为。

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