2021
DOI: 10.3390/s21155231
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Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Abstract: Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive b… Show more

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Cited by 18 publications
(14 citation statements)
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“…The sound quality obtained by placing a microphone on the forehead of the animal enabled algorithm to very precisely discriminate the sounds associated with diverse jaw movements during grazing and rumination. Moreover, this precise detection capability enables to further expand the method to estimate the dry matter intake, to recognize the ingested forage species and also to obtain information about the surrounding environment (Galli et al, 2020;Li et al, 2021).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The sound quality obtained by placing a microphone on the forehead of the animal enabled algorithm to very precisely discriminate the sounds associated with diverse jaw movements during grazing and rumination. Moreover, this precise detection capability enables to further expand the method to estimate the dry matter intake, to recognize the ingested forage species and also to obtain information about the surrounding environment (Galli et al, 2020;Li et al, 2021).…”
Section: Discussionmentioning
confidence: 99%
“…The use of sound to recognize bites, chews, and chew-bites and their individual characteristics allows to obtain precise ingestive information and also to accurately estimate dry matter intake and identify the forage species consumed (Galli et al, 2018(Galli et al, , 2020. By contrast, pressure sensors and accelerometers present diminished performance on automatic monitoring of ingestive behavior (Li et al, 2021;Werner et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…In this vein, Shorten and Hunter 33 found significant variability in cattle vocalization parameters, and suggested that such traits can be monitored using animal-attached acoustic sensors in order to provide information on the welfare and emotional state of the animal. Therefore, automated vocalization monitoring could prove to be a useful tool in precision livestock farming 18,34,35 , especially as dairy farming systems become increasingly automated with wide-scale use of milking and feeding robots, all this having the potential to dynamically adjust the management practices while the number of animals per farm unit tends to increase.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning techniques are therefore increasingly applied in the study of cattle vocalizations. Some tasks that have been addressed to date include classification of high vs. low frequency calls 33 , ingestive behaviour 35 , and categorization of calls such as oestrus and coughs 34 .…”
Section: Introductionmentioning
confidence: 99%
“…The BUFAR, the incorporated multilayer perceptron (MLP), achieved F1 scores that were higher than 0.75 for both grazing and rumination in the 5-minute detection window size, which outperformed a commercial rumination time estimation system [45]. Similarly, Li et al (2021) revealed that the technique for combining collected sound data with deep-learning algorithms could monitor dairy cow feeding behaviors (bites, chews, and chew-bites) and forage species (alfalfa vs. tall fescue) and heights (tall and short) significantly influenced the amplitude and duration of the feeding sounds of dairy cows [48]. Although sound sensors have good performance for monitoring chewing behavior, they are susceptible to being affected by noise in complex farms [49].…”
mentioning
confidence: 99%