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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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LDA Based Tea Leaf Classification on the Basis of Shape, Color and Texture

ANAMIKA SHARMA, PARUL MALHOTRA, , ,
Affiliations
CSE, Sri Sai University ,Palampur, H.P, India
:10.22362/ijcert/2017/v4/i12/xxxx [UNDER PROCESS]


Abstract
Background/Objectives: The presented paper shows a model of leaf segmentation for tea leaf, its seem to be a promising and feasible approach to perform the task of detecting arbitrary shapes in a tea leaf image with a minimum prior. The performance for given image samples was satisfying. Methods/Statistical analysis: Traditional models were very easy to use in but they did not detect boundaries very accurately. On the other hand proposed algorithm was able to detect boundaries well and will be enhanced with image blending to prove the effectiveness of the technique in real applications. Findings: The results have been displayed in the result section with comparison to previous system in terms of area, time and efficiency. Improvements/Applications: In the proposed LDA system accuracy has been improved.


Citation
ANAMIKA SHARMA and PARUL MALHOTRA (2017). LDA Based Tea Leaf Classification on the Basis of Shape, Color and Texture. International Journal of Computer Engineering In Research Trends, 4(12), 543-546. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1201.pdf


Keywords : Segmentation, LDA, PDE, SVM, RGB

References
1.N. Krishnan, C. Nelson Kennedy Babu, V.V. Joseph Raja pandi anand N. Richard Devaraj, A Fuzzy Image Segmentation using Feed forward Neural Networks with Supervised Learning. Proceedings of the International Conference on Cognition and Recognition.
2.	R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann,S. S. Choi, and J. S. Werner, Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets.Journal of Biomedical Optics, vol. 12, no. 4, pp. 041 206, 1–8, 2007.
3.	A.R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, and B. Hamann, Segmentation of three-dimensional retinal image data. . IEEE Transactions on, vol. 13, no. 6, pp. 1719–1726, 2007	
4.	Jayamala K. Patil, Raj Kumar, “Advances In Image Processing For Detection of Plant Diseases” JABAR,vol. 2(2), pp. 135-141, June-2011.
5.	Mr. Pramod and S. landge , Automatic Detection and Classification of Plant Disease through Image Processing. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, ISSN: 2277 128X, 2013.
6.	S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini, Detection of unhealthy region ofplant leaves and classification of plant leaf diseases using texture features. CIGR, vol. 15(1), pp. 211-217, March 2013.
7.	Dodla. Likhith Reddy, Dr. D Prathyusha Reddy.” Texture Image Segmentation Based on threshold Techniques. “International Journal of Computer Engineering in Research Trends., vol.4, no.3, pp. 69-75, 2017.
8.	Venkata Srinivasu Veesam, Bandaru Satish Babu.” A Relative Study on the Segmentation Techniques of Image Processing.“International Journal of Computer Engineering in Research Trends., vol.4, no.5, pp. 155-160, 2017.


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DOI:10.22362/ijcert