Development of an early warning algorithm to detect sick broilers

Development of an early warning algorithm to detect sick broilers •The aim of the paper was to automatically diagnose the health status of broilers.•K-means clustering and the ellipse model were used to segment the images.•Seven features were identified to recognize the health status of broilers.•The SVM model, using the POLY kernel function, obtains an accuracy rate of 99.469%.•The algorithm achieves the purpose of early warning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers and Electronics in Agriculture Elsevier

Development of an early warning algorithm to detect sick broilers

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0168-1699
eISSN
1872-7107
D.O.I.
10.1016/j.compag.2017.11.032
Publisher site
See Article on Publisher Site

Abstract

•The aim of the paper was to automatically diagnose the health status of broilers.•K-means clustering and the ellipse model were used to segment the images.•Seven features were identified to recognize the health status of broilers.•The SVM model, using the POLY kernel function, obtains an accuracy rate of 99.469%.•The algorithm achieves the purpose of early warning.

Journal

Computers and Electronics in AgricultureElsevier

Published: Jan 1, 2018

References

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