Classification of tea grains based upon image texture feature analysis under different illumination conditions

Laddi, Amit and Sharma, Shashi and Kumar, Amod and Kapur, Pawan (2013) Classification of tea grains based upon image texture feature analysis under different illumination conditions. Journal of Food Engineering, 115 (2). pp. 226-231. ISSN 0260-8774

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Official URL: http://www.sciencedirect.com/science/article/pii/S...

Abstract

This paper discusses the role of illumination in discrimination of tea samples based upon textural features of tea granules. The images of tea granules were acquired using 3CCD color camera under Dual Ring light which consists of both Darkfield as well as Brightfield type of illumination. Ten graded tea samples were analyzed. Five textural features were ‘entropy’, ‘contrast’, ‘homogeneity’, ‘correlation’ and ‘energy’ obtained under both illuminations. The acquired textural features were subjected to principal component analysis (PCA). The results showed that best discrimination was obtained with Darkfield illumination with a variance of 96% whereas Brightfield illumination showed low discrimination with only 83% variance. Analysis of PCA biplot indicated correlations among graded tea samples and textural features. The study concludes that textural features may be used to estimate tea quality under Darkfield illumination being non-destructive and quick technique.

Item Type: Article
Uncontrolled Keywords: Darkfield; Brightfield; Textural features; Machine vision; PCA
Subjects: CSIO > Agri - Instrumentation
Divisions: Agri - Instrumentation
Depositing User: Ms. J Shrivastav
Date Deposited: 29 Apr 2013 11:54
Last Modified: 29 Apr 2013 11:54
URI: http://csioir.csio.res.in/id/eprint/359

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