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Automatic recognition of jaw movements in free-ranging cattle, goats and sheep, using acoustic monitoring
Year:
2013
Source of publication :
Biosystems Engineering
Authors :
Hetzroni, Amots
;
.
Mizrach, Amos
;
.
Navon, Shilo
;
.
Ungar, Eugene David
;
.
Volume :
114
Co-Authors:
Navon, S., Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization - The Volcani Center, P.O. Box 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
Hetzroni, A., Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization - The Volcani Center, POB 6, Bet Dagan 50250, Israel
Ungar, E.D., Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization - The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Facilitators :
From page:
474
To page:
483
(
Total pages:
10
)
Abstract:
Sensor technologies to quantify the feeding behaviour of free-grazing domesticated herbivores are required. Acoustic monitoring is a promising method, but signal processing algorithms to automatically identify and classify sound-producing jaw movements are not well developed.Wepresent an algorithmfor jawmovement identification that is designed to be as general as possible; it requires no calibration and identifies jaw movements according to key features in the timedomain that are defined in relative terms.Amachine-learning approach is used to separate true jaw-movement sounds from background noise and intense spurious noises. The algorithmsoftware performance was tested in three field studies by comparing its output with that generated by aural sequencing. For cattle grazing green pasture in a lownoise environment with a Lavalier microphone positioned on the forehead, the system achieved 94% correct identification (i.e., aural events matched by software events within a tolerance of 0.2 s) and a false positive rate (i.e., software events not similarly matched by aural events) of 7%. For goats grazing green herbage in an extremely noisy environment, and with a piezoelectric microphone positioned on the horn, the system achieved 96% correct identification and 4% false positives. For sheep grazing dry pasture in an environment characterised by frequent intense noises, and with a piezoelectric microphone positioned on the horn, the systemachieved 84%correct identification and 24%false positives. Very lowerror rates can be obtained from the software if intense extraneous noises can be avoided. © 2012 IAgrE.
Note:
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More details
DOI :
10.1016/j.biosystemseng.2012.08.005
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
29176
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:44
Scientific Publication
Automatic recognition of jaw movements in free-ranging cattle, goats and sheep, using acoustic monitoring
114
Navon, S., Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization - The Volcani Center, P.O. Box 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
Hetzroni, A., Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization - The Volcani Center, POB 6, Bet Dagan 50250, Israel
Ungar, E.D., Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization - The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Automatic recognition of jaw movements in free-ranging cattle, goats and sheep, using acoustic monitoring
Sensor technologies to quantify the feeding behaviour of free-grazing domesticated herbivores are required. Acoustic monitoring is a promising method, but signal processing algorithms to automatically identify and classify sound-producing jaw movements are not well developed.Wepresent an algorithmfor jawmovement identification that is designed to be as general as possible; it requires no calibration and identifies jaw movements according to key features in the timedomain that are defined in relative terms.Amachine-learning approach is used to separate true jaw-movement sounds from background noise and intense spurious noises. The algorithmsoftware performance was tested in three field studies by comparing its output with that generated by aural sequencing. For cattle grazing green pasture in a lownoise environment with a Lavalier microphone positioned on the forehead, the system achieved 94% correct identification (i.e., aural events matched by software events within a tolerance of 0.2 s) and a false positive rate (i.e., software events not similarly matched by aural events) of 7%. For goats grazing green herbage in an extremely noisy environment, and with a piezoelectric microphone positioned on the horn, the system achieved 96% correct identification and 4% false positives. For sheep grazing dry pasture in an environment characterised by frequent intense noises, and with a piezoelectric microphone positioned on the horn, the systemachieved 84%correct identification and 24%false positives. Very lowerror rates can be obtained from the software if intense extraneous noises can be avoided. © 2012 IAgrE.
Scientific Publication
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