Projects per year
Abstract
The reticuloruminal function is central to the digestive efficiency in ruminants. For cattle, collar- and ear tag-based accelerometer monitors have been developed to assess the time spent ruminating on an individual animal. Cattle that are ill feed less and so ruminate less, thus, the estimation of the time spent ruminating provides insights into the health of individual animals. pH boluses directly provide information on the reticuloruminal function within the rumen and extended (three hours or more) periods during which the ruminal pH value remains below 5.6 is an indicator that dysfunction and poor welfare are likely. Accelerometers, incorporated into the pH boluses, have been used to indicate changes in behaviour patterns (high/low activity), utilised to detect the onset of oestrus. The paper demonstrates for the first time that by processing the reticuloruminal motion, it is possible to recover rumination periods. Reticuloruminal motion energy and the time between reticuloruminal contractions are used as inputs to a Support Vector Machine (SVM) to identify rumination periods with an overall accuracy of 86.1%, corroborated by neck mounted rumination collars.
Original language | English |
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Article number | 1165 |
Number of pages | 14 |
Journal | Sensors |
Volume | 19 |
Issue number | 5 |
DOIs | |
Publication status | Published - 7 Mar 2019 |
Keywords
- cattle
- bolus sensors
- accelerometers
- behaviour
- rumination
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Dive into the research topics of 'Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensors'. Together they form a unique fingerprint.Projects
- 1 Finished
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PrecisionBeef
Michie, C. (Principal Investigator) & Andonovic, I. (Co-investigator)
BBSRC (Biotech & Biological Sciences Research Council)
1/04/15 → 30/06/18
Project: Research
Datasets
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Bolus acceleration data 3x
Hamilton, A. (Creator), Michie, W. (Creator), Davison, C. (Creator), Andonovic, I. (Creator) & Tachtatzis, C. (Creator), University of Strathclyde, 27 Feb 2019
DOI: 10.15129/fc809102-7288-4b4e-aa48-55ba6608dc2c
Dataset