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

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskning

Standard

Daily eating activity of dairy cows from 3D accelerometer data and RFID signals: prediction by random forests and detection of sick cows. / Foldager, Leslie; Gildbjerg, Lars Bilde; Voss, Heidi; Trénel, Philipp; Munksgaard, Lene; Thomsen, Peter T.

Symposium i Anvendt Statistik. red. / Peter Linde. 2017. s. 109-122.

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskning

Harvard

Foldager, L, Gildbjerg, LB, Voss, H, Trénel, P, Munksgaard, L & Thomsen, PT 2017, Daily eating activity of dairy cows from 3D accelerometer data and RFID signals: prediction by random forests and detection of sick cows. i P Linde (red.), Symposium i Anvendt Statistik. s. 109-122, Symposium i Anvendt Statistik, Odense, Danmark, 23/01/2017.

APA

CBE

MLA

Vancouver

Author

Bibtex

@inproceedings{9f097cb83e194535b97fb4cc75948351,
title = "Daily eating activity of dairy cows from 3D accelerometer data and RFID signals: prediction by random forests and detection of sick cows",
abstract = "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.",
author = "Leslie Foldager and Gildbjerg, {Lars Bilde} and Heidi Voss and Philipp Tr{\'e}nel and Lene Munksgaard and Thomsen, {Peter T.}",
year = "2017",
month = "1",
language = "English",
isbn = "978-87-501-2267-8",
pages = "109--122",
editor = "Peter Linde",
booktitle = "Symposium i Anvendt Statistik",

}

RIS

TY - GEN

T1 - Daily eating activity of dairy cows from 3D accelerometer data and RFID signals: prediction by random forests and detection of sick cows

AU - Foldager, Leslie

AU - Gildbjerg, Lars Bilde

AU - Voss, Heidi

AU - Trénel, Philipp

AU - Munksgaard, Lene

AU - Thomsen, Peter T.

PY - 2017/1

Y1 - 2017/1

N2 - 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.

AB - 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.

M3 - Article in proceedings

SN - 978-87-501-2267-8

SP - 109

EP - 122

BT - Symposium i Anvendt Statistik

A2 - Linde, Peter

ER -