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Heat event detection in dairy cows with collar sensors: An unsupervised machine learning approach


Shahriar, MS and Smith, D and Rahman, A and Henry, D and Bishop-Hurley, G and Rawnsley, R and Freeman, M and Hills, J, Heat event detection in dairy cows with collar sensors: An unsupervised machine learning approach, Proceedings of 2015 IEEE SENSORS, 1-4 November 2015, Busan, South Korea, pp. 1-4. ISBN 978-1-4799-8202-8 (2015) [Refereed Conference Paper]

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Copyright 2015 IEEE

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DOI: doi:10.1109/ICSENS.2015.7370528


The detection of heat (estrus) events in pasture-based dairy cows fitted with on-animal sensors was investigated using an unsupervised learning. Accelerometer data from the cow collar sensors were used in this approach where the aim was to identify increased activity level (restlessness, increased walking for mating) and to find association with recorded heat events. High dimensional time series data from accelerometers were first segmented in windows followed by feature extractions. The extracted features are standard deviation, amplitude, energy and Fast Fourier Transform (FFT). K-means clustering algorithm was then applied across the windows for grouping. The groups were labeled in terms of activity intensities: high, medium and low. An activity index level (AIxL) was derived from the activity intensity labels. We compared the AIxL with recorded heat events and observed significant associations between the increased activities through high AIxL values and the observed heat events.

Item Details

Item Type:Refereed Conference Paper
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Animal production
Research Field:Animal management
Objective Division:Animal Production and Animal Primary Products
Objective Group:Livestock raising
Objective Field:Dairy cattle
UTAS Author:Rawnsley, R (Dr Richard Rawnsley)
UTAS Author:Freeman, M (Mr Mark Freeman)
UTAS Author:Hills, J (Dr James Hills)
ID Code:109955
Year Published:2015
Deposited By:Tasmanian Institute of Agriculture
Deposited On:2016-07-08
Last Modified:2016-08-09

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