4.7 Article

The effect of different time epoch settings on the classification of sheep behaviour using tri-axial accelerometry

期刊

COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 154, 期 -, 页码 112-119

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ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2018.09.002

关键词

Feeding behaviour; Accelerometer; Wireless communication technology; Discriminant analysis

资金

  1. Government of Sardinia (Italy) [CRP-17287 PO Sardegna, FSE 2007-2013 LR 7/2007]

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Monitoring behaviour of grazing animals is important for the management of grazing systems. A study was run to discriminate between the main behaviours (grazing, ruminating and other activities) of sheep at pasture wearing a halter equipped with an accelerometer (BEHARUM device), and to identify the epoch setting (5, 10, 30, 60, 120, 180 and 300 s) with the best performance. The BEHARUM device includes a three-axial accelerometer sensor and a force sensor positioned under the lower jaw of the animal. The halter was fitted to eight Sarda dairy sheep that rotationally grazed either a spatial association (mixture) or a time association of berseem clover (Trifolium alexandrinum L.) and Italian ryegrass (Lolium multiflorum Lam.) for 6 h day(-1). The behaviour of the animals was also video-recorded. The raw acceleration and force data were processed for each epoch setting to create 15 variables: the mean, variance and inverse coefficient of variation (ICV; mean/standard deviation) per minute for the X-, Y-, Z-axis and force, and the resultant. Multivariate statistical techniques were used to discriminate between the three behavioural activities: canonical discriminant analysis (CDA), and discriminant analysis (DA). To validate the derived discriminant functions, a bootstrap procedure was run. To evaluate the performance of DA in discriminating between the three activities, the sensitivity, specificity, precision, accuracy and Coehn's k coefficient were calculated, based on the error distribution in assignment. Results show that a discriminant analysis can accurately classify important behaviours such as grazing, ruminating and other activities in sheep at pasture. The prediction model has demonstrated a better performance in classifying grazing behaviour than ruminating and other activities for all epochs. The 30 s epoch length yielded the most accurate classification in terms of accuracy and Coehn's k coefficient. Nevertheless, 60 and 120 s may increase the potential recording time without causing serious lack of accuracy.

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