Abstract
An SVM with texture analysis-based feature extraction classification method is presented for identification of fresh tea leaves in this paper. This method is proved to be very efficient and effective in the identification of fresh tea leaves through real experiments. First, the texture characteristic parameters of tea leave images are obtained by texture feature extraction. After that, different categories of fresh tea leaves are identified through SVM training. These texture parameters for texture classification include energy, correlation, and contrast obtained from gray-level co-occurrence matrix (GLCM). Experimental results show that the use of SVM for classification of tea leaves can achieve very good results, and the successful classification rate can be as high as 83 %.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Li XL, He Y (2009) Classification of tea grades by multi-spectral images and combined feature. Trans Chin Soc Agric Mach S1:113–118
Chen Q, Zhao J, Fang CH et al (2007) Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochim Acta Part A Mol Biomol Spectrosc 66(3):568–574
Wu D, Yang H, Chen X et al (2008) Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine. J Food Eng 88(4):474–483
Chen Y, Chang C, Xiao H et al (2010) Artificial neural networks technology in the fresh tea sorting. J Agric Netw Inform 7:013
Gill GS, Kumar A, Agarwal R (2013) Nondestructive grading of black tea based on physical parameters by texture analysis. Biosyst Eng 116(2):198–204
Du CJ, Sun DW (2004) Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15(5):230–249
Chen Y, Wang JZ (2003) Support vector learning for fuzzy rule-based classification systems. Fuzzy Syst IEEE Trans 11(6):716–728
Li WB, Chen YY (2010) Using fisher linear discriminate analysis to extracting classifiers. Comput Eng Appl 46(14):132–134
Zhang X, Yan W, Zhao X et al (2007) Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis. Process Biochem 42(8):1200–1210
Hwang WJ, Wen KW (1998) Fast kNN classification algorithm based on partial distance search. Electron Lett 34(21):2062–2063
Wei CT, Wang N, Zhang LH et al (2013) Remote sensing image classification based on texture features. J Guilin Univ Technol 1:016
Zhong J, Ming H (2009) Bayes decision for minimum errors applied in recognition of handwritten english letters. J Liaoning Univ Technol (Nat Sci Ed) 29(2):98–101
Acknowledgments
This work is supported by the Natural Science Foundation of China (31470028, 61103035), Funds of Science and Technology Plan in Hunan province (2014GK3029), Funds of Science and Technology Plan in Changsha city (K1306035-11), the Project of Hunan Strategic Emerging Industries (2014GK1020). Authors are grateful for the anonymous reviewers who made constructive comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, Z., Qi, F., Zhou, Y., Pan, F., Zhou, J. (2015). Tea Leaves Classification Based on Texture Analysis. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-662-46469-4_37
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46468-7
Online ISBN: 978-3-662-46469-4
eBook Packages: EngineeringEngineering (R0)