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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Verma, B.: Image processing techniques for grading & classification of rice. In: 2010 International Conference on Computer and Communication Technology, pp. 220–223 (2010)
Yao, Q., Zhou, Y., Wang, J.: An automatic segmentation algorithm for touching rice grains images. In: 2010 International Conference on Audio Language and Image Processing, pp. 802–805 (2010)
Hobson, D.M., Carter, R.M., Yan, Y.: Characterisation and identification of rice grains through digital image analysis. In: Instrumentation and Measurement Technology Conference Proceedings 2007, pp. 1–5. IEEE Press, New York (2007)
Yao, M., Liu, M., Zheng, H.: Exterior quality inspection of rice based on computer vision. In: World Automation Congress 2010, pp. 369–374 (2010)
Suykens, J.A.K., Vandewalle, J.: Multiclass least squares support vector machines. In: International Joint Conference on Neural Networks, pp. 900–903 (1999)
Kuh, A.: Analysis of detectors for support vector machines and least square support vector machines. In: International Joint Conference on Neural Networks, pp. 1075–1079 (2002)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Pearson Publishing Group, USA (1989)
Guo, P., Wang, X., Han, Y.: The enhanced genetic algorithms for the optimization design. In: 3rd International Conference on Biomedical Engineering and Informatics, pp. 2990–2994 (2010)
Yao, M., Liu, M., Zheng, H.: Exterior quality inspection of rice based on computer vision. In: World Automation Congress (WAC), pp. 369–374 (2010)
Modi, C.K., Jain, K.R.: Quality Evaluation of Hydrothermal Treated Quicker Cooking Scented Rice by Quantification of Quickness of Cooking Time and Mechanical Strength Using Machine Vision. In: 2011 International Conference on Digital Computational Intelligence and Communication Networks (CICN), pp. 639–643 (2011)
Dai, X., Xiong, D.: Research on conjoint grains of rice based on machine vision. In: 2nd International Conference on Signal Processing Systems (ICSPS), pp. 760–763 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)