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Real-time monitoring of poultry activity in breeding farms

Published: 29 October 2017 Publication History

Abstract

Decreasing profit margins and increasing concerns about animal welfare are boosting the interest for the development of monitoring and analysis technologies specifically targeting the poultry meat production process. In this context, this paper addresses monitoring of poultry activity in breeding farms. Specifically, it analyzes the suitability of different vision systems and image processing algorithms with this purpose. These systems and algorithms have been tested in an actual farm during a breeding cycle. Experimental results are presented demonstrating that density-based computations provide the best results, and that they can be carried out using either video or thermographic images, but the latter are a better option because of practical operating reasons related to varying, low light intensity conditions.

References

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Food and Agricultural Organization of the United Nations (FAO), Poultry and welfare. Online: http://www.fao.org/ag/againfo/themes/en/poultry/AH_welfare.html. Accessed on 05/16/2017.
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C.T. Usher et al., “Signal processing for animal behavior detection”, in Proc. 2014 IEEE Global Conference on Signal and Information Processing, pp. 1145–1149, 2014.
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N.S. Amir et al., “Chicken farm monitoring system”, in Proc. 2016 International Conference on Computer & Communication Engineering, pp. 132–137, 2016.
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M.S. Dawkins, R. Cain, and S.J. Roberts, “Optical flow, flock behaviour and chicken welfare”, Animal Behaviour, vol. 84, no. 1, pp. 219–223, Jul. 2012.
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R.R. Curtin, W. Daley, and D.V. Anderson, “Classifying broiler chicken condition using audio data”, in Proc. 2014 IEEE Global Conference on Signal and Information Processing, pp. 1141–1144, 2014.
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Weixing Zhu, Yansong Peng, and Bin Ji, “An automatic dead chicken detection algorithm based on SVM in modern chicken farm”, in Proc. Second International Symposium on Information Science and Engineering, pp. 323–326, 2009.
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Md.B.R. Mollah et al., “Digital image analysis to estimate the live weight of broiler”, Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 48–52, Jun. 2010.
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cover image Guide Proceedings
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Oct 2017
8455 pages

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IEEE Press

Publication History

Published: 29 October 2017

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