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

文献类型： 外文期刊

第一作者： Guo, Leifeng

作者： Guo, Leifeng;Wang, Wensheng;Welch, Mitchell;Kwan, Paul;Dobos, Robin;Welch, Mitchell;Dobos, Robin

作者机构：

关键词： Grazing behaviour; Inertial measurement unit; Sward surface heights; Linear discriminant analysis; Sheep

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2018 年 150 卷

页码：

收录情况： SCI

摘要： 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.

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