Abstract
A machine vision system was developed for rice quality detection in this paper. The main characteristics of rice appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. The Least Squares Support Vector Machines was applied for the classification of head rice and broken rice. Genetic algorithm was used to optimize the parameters values of Least Squares Support Vector Machines. The robustness of this classification method was testified, and the experiment result shows that the head rice and broken rice can be effectively identified by Least Squares Support Vector Machines using machine vision.
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© 2012 Springer-Verlag Berlin Heidelberg
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Chen, X., Ke, S., Wang, L., Xu, H., Chen, W. (2012). Classification of Rice Appearance Quality Based on LS-SVM Using Machine Vision. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34038-3_15
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DOI: https://doi.org/10.1007/978-3-642-34038-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34037-6
Online ISBN: 978-3-642-34038-3
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