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Heat event detection in dairy cows with collar sensors: An unsupervised machine learning approach
conference contribution
posted on 2023-05-23, 11:14 authored by Shahriar, MS, Smith, D, Rahman, A, Henry, D, Bishop-Hurley, G, Richard RawnsleyRichard Rawnsley, Mark FreemanMark Freeman, James HillsJames HillsThe 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.
History
Publication title
Proceedings of 2015 IEEE SENSORSEditors
K B OzanyanPagination
1-4ISBN
978-1-4799-8202-8Department/School
Tasmanian Institute of Agriculture (TIA)Publisher
IEEEPlace of publication
NJ, USAEvent title
2015 IEEE SENSORSEvent Venue
Busan, South KoreaDate of Event (Start Date)
2015-11-01Date of Event (End Date)
2015-11-04Rights statement
Copyright 2015 IEEERepository Status
- Restricted