https://doi.org/10.4081/jae.2014.418

Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle

Vol. 45 No. 4 (2014)
Submitted: 5 July 2014
Accepted: 6 October 2014
Published: 21 December 2014
speckles, remote sensing, cattle identification, search methods.
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Authors

Yevgeny Beiderman
Institute of Agricultural Engineering, Agricultural Research Organisation (ARO), the Volcani Center, Bet-Dagan, Israel.
Mark Kunin
Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
Eli Kolberg
eli.kolberg@biu.ac.il
Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
Ilan Halachmi
Institute of Agricultural Engineering, Agricultural Research Organisation (ARO), the Volcani Center, Bet-Dagan, Israel.
Binyamin Abramov
Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
Rafael Amsalem
Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
Zeev Zalevsky
Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. The sensor is used on top of an automated robotic platform in order to support animal decision making. Field tests and computer simulated results are presented.

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How to Cite

Beiderman, Y., Kunin, M., Kolberg, E., Halachmi, I., Abramov, B., Amsalem, R. and Zalevsky, Z. (2014) “Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle”, Journal of Agricultural Engineering, 45(4), pp. 153–160. doi: 10.4081/jae.2014.418.

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