Prediction of tail biting events in finisher pigs from automatically recorded sensor data
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Prediction of tail biting events in finisher pigs from automatically recorded sensor data. / Larsen, Mona Lilian Vestbjerg; Pedersen, Lene Juul; Jensen, Dan Børge.
I: Animals, Bind 9, Nr. 7, 458, 2019.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Prediction of tail biting events in finisher pigs from automatically recorded sensor data
AU - Larsen, Mona Lilian Vestbjerg
AU - Pedersen, Lene Juul
AU - Jensen, Dan Børge
PY - 2019
Y1 - 2019
N2 - Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.
AB - Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.
KW - Artificial neural network
KW - Bayesian ensemble
KW - Bayes’ Theorem
KW - Computational ethology
KW - Drinking behaviour
KW - Dynamic linear models
KW - Pen temperature
KW - Precision livestock farming
KW - Sus scrofa domesticus
KW - Water flow
U2 - 10.3390/ani9070458
DO - 10.3390/ani9070458
M3 - Journal article
C2 - 31330973
AN - SCOPUS:85071361208
VL - 9
JO - Animals
JF - Animals
SN - 2076-2615
IS - 7
M1 - 458
ER -
ID: 227873417