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.
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.