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Quality Evaluation of Tea Leaves during Fermentation using MRSMRFM

Dr.R.S. Sabeenian

Abstract


Tea Industries in turn process the tea leaves for exporting the tea production. Quality of the tea is very important. However tea color determination during fermentation is a vital problem in the tea industries. This makes a major contribution of the quality of tea. The human experts since the beginning of the tea industry have been traditionally measuring tea color and flavor to detect the optimum fermenting condition. They use visual inspection and smelling or tasting method which is purely subjective, invasive, time consuming and inexact due to various reasons such as individual variability, adaptation, infection, mental state etc. Chemical analysis is also performed on the tea leaves to determine the fermentation condition. This in turn degrades the quality of the tea. The grading of the tea also differs. Also whenever the leaves are fermented more, they are not used for exporting and hence there is more wastage in the processing. Hence we implement non-destructive testing of tea leaves by using image processing technique. The proposed Multi Resolution Statistical Markov Random Field Matrix (MRSMRFM) method is a combination of first order and second order statistical and spectral features used to test and determine the optimal fermenting condition.

Keywords


Texture, Tea Leaves, Wavelet Transform, Markov Random Field (MRF), Gray Level Co Occurrence Method (GLCM), MultiResolution Statistical Markov Random Field Matrix (MRSMRFM).

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References


H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh “Fast and Accurate Detection and Classification of Plant Diseases” International Journal of Computer Applications (0975 – 8887) Volume 17– No.1, March 2011

R.S. Sabeenian, V.Palanisamy, “Crop and Weed Discrimination in Agricultural Field using MRCSF”, International Journal of of Signal and Imaging Systems Engineering, Vol.5, No.10, pp.420-429.

R.S. Sabeenian, V. Palanisamy, “Texture Based Medical Image Classification of Computed Tomography Images using MRCSF”, International Journal of Medical Engineering and Informatics Published by Inderscience Publications (IJMEI), 1, No. 4 pp. 459-472.

R.S. Sabeenian, M. E. Paramasivam, “Handloom Silk Fabric Defect Detection using First order Statistical Features on a NIOS II Processor‟ Published in the Springer International Conference on Advances in Information and Communication Technologies ICT 2010 held on September 2010 at Cochin, India, 2010, Volume 101, Part 3, pp 475-477, DOI: 10.1007/978-3-642-15766-0_77.

R.S. Sabeenian, V. Palanisamy, “Comparison of efficiency for texture image classification using MRMRF & GLCM techniques”, published in International Journal of Computers Information Technology and Engineering (IJCITAE), December, Vol. 2, No. 2, pp.87–93, 2008.

T. G. crowe and M.J. delwiche. “Real –time defect in fruit: Part II. An algorithm and performance of a proto type system trans ASAE39(6):2309-2317. 1996.”

J. Edwards, G. Sweet, and C. Haven, “Citrus blight assessment using a microcomputer quantifying damage using an apple computer to solve reflectance spectra of entire trees ”Florida Scientist 49(1)48-53.

Taps kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruthu Silverman, and Angela Y. Wu, “An efficient K-means clustering Algorithm: Analysis and implementation” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, July 2002.

Yiu-Ming Cheung, K-means: “A new generalized K-means clustering algorithm ", Department of computer science, Hong Kong Baptist University, 7/F sir Run Run Shaw Buliding, Kowloon Tong, Hong Kong, Pattern Regognition Letters 24(2003).

Kiri Wagsta, Clarie Cardie, “Constrained K- means Clustering with background Knowledge” proceedings of the Eighteeht International Conference on Machine Learing, 2001,p. 577-584.

M. Mrimehdi and M. Petrou “Segmentation of color Textures” IEEE Transaction on Pattern Analysis and Machine Intelligence, 2(2): 142-159 Feb 2000.

Sabeenian R.S and Dinesh P.M. “Texture Image Classification Using Gray Level Weight Matrix (GLWM)” published in International Conference of Advances in Information Technology and Mobile Communication (AIM‟11) held on April 21-22, 2011 in Nagpur, Maharashtra. Volume 147, Part 2, pp 263-266, DOI: 10.1007/978-3-642-20573-6_43

Youwen, Tian Tianlai, Li Yan, Niu, “ The recognition of cucumber Disease Based on Image Processing and support Vector Machine”, Image and Signal Processing , CISP‟ 08, congress on publication May 2008.

Sabeenian R.S and P.M. Dinesh, „Multi Format Scalable Media Decoder Implementation using OMAP3530‟ Published in the International Conference on Computational Intelligence and Computing Research (ICCIC‟10) held on Dec 2010 at TCE, Coimbatore. pp 616-619, DOI: 10.1109/ICCIC.2010.5705846.

Laine, A., Fan, J., “Texture Classification by the wavelet Packet signature IEEE Transaction , PAMI 15 (11), 1186-1191.

R.S.Sabeenian and M.E.Paramasivam, “Detection and Location of Defects in Handloom Cottage Silk Fabrics using MRMRFM & MRCSF” in the International Journal of Technology And Engineering System (IJTES): Jan – March 2011- Vol.2, No.2, pp 172-176

R.S.Sabeenian and M.E.Paramasivam, “Defect detection and identification in textile fabrics using Multi Resolution Combined Statistical and Spatial Frequency Method” 2010 IEEE International Advance Computing Conference (IACC 2010) held on February 19-20, 2010 at Thapar University, Patiala in collaboration with IEEE Delhi Section and IEEE Computer Society Chapter (IEEE Delhi Section).pp 162-166 Patiala, India. Print ISBN : 978-1-4244-4790-9 .Digital Object Identifier 10.1109/IADCC.2010.54.

R.S.Sabeenian et al, “Computer Vision based Defect Detection and Identification in Handloom Silk Fabrics” in the International Journal of Computer Applications, Volume 42– No.17, March 2012,pp 41-48.

R.S.Sabeenian et al, Identification and Counting of Fertile Pollen Grains using Morphological operators, FSF and CGF in the International Journal of Computer Applications, Volume 42– No.17, March 2012,pp 36-40.

R.S.Sabeenian, M.E.Paramasivam and P.M.Dinesh, “Handloom Silk Defect Recognition and Categorization using Gray Level Weight Matrix & Multi Resolution Combined Statistical and Spatial Frequency Method” published in the Proceedings of ICCAC 2011, held at Cape Institute of Technology, Kanyakumari.

Lecture Notes for DAE-BRNS Workshop on “Applications of Image Processing in Plant Sciences and Agriculture (WIPSA) ” held at Molecular Biology and Agriculture Division, BARC, Oct 26-30, 1998.

Tang, L., L. F. Tian, B. L. Steward, and J. F. Reid. 2011. “Texture based tea leaf classification using gabor wavelets and neural network for real-time applications”. ASAE Paper No. 99-3036.

Borah, S. and Bhuyan, M. (2005), “A computer based system for matching colours during the monitoring of tea fermentation”. International Journal of Food Science & Technology, Vol 40: pp 675–682.

Borah, S. and Bhuyan, M (2003), “Non-destructive testing of tea fermentation using image processing” Volume 45, Number 1, 1 January 2003 , pp. 55-58(4).

Xu, X., Yan, M., Zhu, Y., 2005. “Influence of fungal fermentation on the development of volatile compounds in the Puer tea manufacturing process”. Engineering in Life Sciences 5, 382–386.

Gong, Z., Watanabe, N., Yagi, A., Etoh, H., Sakata, K., Ina, K., Liu, Q., 1993. Compositional change of Pu-erh tea during processing. Bioscience, Biotechnology, and Biochemistry 57, 1745–1746.

Flórez, A.B., Mayo, B., 2006. Microbial diversity and succession during the manufacture and ripening of traditional, Spanish, blue-veined Cabrales cheese, as determined by PCR-DGGE. International Journal of Food Microbiology 110, 165–171.

Camu, N., De Winter, T., Verbrugghe, K., Cleenwerck, I., Vandamme, P., Takrama, J.S.,Vancanneyt, M., De Vuyst, L., 2007. Dynamics and biodiversity of populations of lactic acid bacteria and acetic acid bacteria involved in spontaneous heap fermentation of cocoa beans in Ghana. Applied and Environmental Microbiology 73, 1809–1824.


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