Description
Anomaly detection in the day-to-day activity of dairy cows
is challenging, as true abnormal behavior must be distinguished from
the individual variability and the animals’ endogenous rhythms. Current
algorithms for anomaly detection in times series include various tech
niques, with neural network-based methods being the most prominent.
However, these approaches lack interpretability, which is crucial in precision livestock farming. This work proposes extracting interpretable features using wavelet transforms, enabling better time-frequency analysis of the endogenous rhythm compared to abnormal rhythms. The results show that some wavelets have a positive impact on performance, and align with expert knowledge.
Auteurs
Mme
Isabelle Veissier
(INRAE)
M.
Luis Rocha
(Ghent University)
M.
Romain Lardy
(INRAE)
Valentin Guien
(LIMOS)
Mme
Violaine Antoine
(LIMOS)