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Ungar, E.D., Department of Field Crops and Natural Resources, Agricultural Research Organization-the Volcani Center, Institute of Agricultural Engineering, POB 6, Bet Dagan, 50250, Israel
Blankman, J., Department of Field Crops and Natural Resources, Agricultural Research Organization-the Volcani Center, Institute of Agricultural Engineering, POB 6, Bet Dagan, 50250, Israel
Mizrach, A., Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization-the Volcani Center, POB 6, Bet Dagan, 50250, Israel
A convenient method of monitoring ingestive behaviour in grazing animals is lacking. Acoustic monitoring is a promising technology that could provide accurate and continuous information on ingestive behaviour. A critical step is to develop a signal processing algorithm to automatically classify sound bursts into bite, chew and chew-bite jaw movements. We tested the classification of jaw movements on the basis of six extracted features. Discriminant analysis achieved a correct classification rate of 81.8% using four of the six features. These results suggest that acoustic monitoring may be a simple and effective way of quantifying key aspects of ingestive behaviour in grazing ruminants.
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The classification of herbivore jaw movements using acoustic analysis
Ungar, E.D., Department of Field Crops and Natural Resources, Agricultural Research Organization-the Volcani Center, Institute of Agricultural Engineering, POB 6, Bet Dagan, 50250, Israel
Blankman, J., Department of Field Crops and Natural Resources, Agricultural Research Organization-the Volcani Center, Institute of Agricultural Engineering, POB 6, Bet Dagan, 50250, Israel
Mizrach, A., Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization-the Volcani Center, POB 6, Bet Dagan, 50250, Israel
The classification of herbivore jaw movements using acoustic analysis
A convenient method of monitoring ingestive behaviour in grazing animals is lacking. Acoustic monitoring is a promising technology that could provide accurate and continuous information on ingestive behaviour. A critical step is to develop a signal processing algorithm to automatically classify sound bursts into bite, chew and chew-bite jaw movements. We tested the classification of jaw movements on the basis of six extracted features. Discriminant analysis achieved a correct classification rate of 81.8% using four of the six features. These results suggest that acoustic monitoring may be a simple and effective way of quantifying key aspects of ingestive behaviour in grazing ruminants.
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