A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring
2024
Dissanayake, Oshana | Riaboff, Lucile | Mcpherson, Sarah, E. | Kennedy, Emer | Cunningham, Pádraig | University College Dublin [Dublin] (UCD) | VistaMilk SFI Research Centre Moorepark, Fermoy ; Teagasc Food Research Centre [Fermoy, Ireland] | Génétique Physiologie et Systèmes d'Elevage (GenPhySE) ; Ecole Nationale Vétérinaire de Toulouse (ENVT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Teagasc - The Agriculture and Food Development Authority (Teagasc) | Wageningen University and Research [Wageningen] (WUR) | This publication has emanated from research conducted with the financial support of SFI and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland to the VistaMilk SFI Research Centre under Grant Number 16/RC/3835.
International audience
Показать больше [+] Меньше [-]Английский. In recent years there has been considerable progress in research on human activity recognition using data from wearable sensors. This technology also has potential in an animal welfare context in livestock science. In this paper we report on research on animal activity recognition in support of welfare monitoring. The data comes from collar-mounted accelerometer sensors worn by Holstein and Jersey calves; the objective being to detect changes in behaviour indicating sickness or stress. A key requirement in detecting changes in behaviour is to be able to classify activities into classes such as drinking, running or walking. In Machine Learning terms, this is a time-series classification task and in recent years the Rocket family of methods have emerged as the state-of-the-art in this area. For our analysis we have over 27 hours of labelled time-series data from 30 calves. We present the performance of Rocket on a 6-class classification task on this data as a baseline. Then we compare this against the performance of 11 Deep Learning (DL) methods that have been proposed as promising methods for time-series.
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