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Lars Bilde Gildbjerg

Daily eating activity of dairy cows from 3D accelerometer data and RFID signals: prediction by random forests and detection of sick cows

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearch

Feed intake is very important for dairy cows and deviation from normal eating behaviour may predict a cow that needs treatment. We used video recordings of dairy cows at the Danish Cattle Research Centre (DKC) combined with data from a neck-collar mounted 3D accelerometer and RFID device from Lyngsoe Systems (Aars, Denmark) to develop a random forests model for predicting daily eating activity. We investigated performance by internal cross-validation and the results indicate that we obtain accurate predictions of daily eating time by the algorithm. Technical challenges are delaying the planned tests on commercial farms. We are therefore currently utilising historical data from DKC to examine the potential of using changes in daily eating time for detection of sick cows.
Original languageEnglish
Title of host publicationSymposium i Anvendt Statistik
EditorsPeter Linde
Publication yearJan 2017
ISBN (print)978-87-501-2267-8
Publication statusPublished - Jan 2017
EventSymposium i Anvendt Statistik - Syddansk Universitet, Odense, Denmark
Duration: 23 Jan 201724 Jan 2017
Conference number: 39


ConferenceSymposium i Anvendt Statistik
LocationSyddansk Universitet

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