Tea category classification using morphological characteristics and support vector machines

X. L. Li; Y. He; Z. J. Qiu; Y. D. Bao

doi:10.1117/12.821889

10 February 2009 Tea category classification using morphological characteristics and support vector machines

X. L. Li, Y. He, Z. J. Qiu, Y. D. Bao

Proceedings Volume 7126, 28th International Congress on High-Speed Imaging and Photonics; 712613 (2009) https://doi.org/10.1117/12.821889
Event: 28th International Congress on High-Speed Imaging and Photonics, 2008, Canberra, Australia

Abstract

Tea categories classification is an importance task for quality inspection. And traditional way for doing this by human is time-consuming, requirement of too much manual labor. This study proposed a method for discriminating green tea categories based on multi-spectral images technique. Four tea categories were selected for this study, and total of 243 multi-spectral images were collected using a common-aperture multi-spectral charged coupled device camera with three channels (550, 660 and 800 nm). A compound image which has the clearest outline of samples was process by combination of the three monochrome images (550, 660 and 800 nm). After image preprocessing, 18 morphometry parameters were obtained for each samples. The 18 parameters used including area, perimeter, centroid and eccentricity et al. To better understanding these parameters, principal component analysis was conducted on them, and score plot of the first three independent components was obtained. The first three components accounted for 99.02% of the variation of original 18 parameters. It can be found that the four tea categories were distributed in dense clusters respectively in score plot. But the boundaries among them were not clear, so a further discrimination must be developed. Three algorithms including support vector machines, artificial neural network and linear discriminant analysis were adopted for developed classification models based on the optimized 9 features. Wonderful result was obtained by support vector machines model with accuracy of 93.75% for prediction unknown samples in testing set. It can be concluded that it is an effective method to classification tea categories based on computer vision, and support vector machines is very specialized for development of classification model.

Citation Download Citation

X. L. Li, Y. He, Z. J. Qiu, and Y. D. Bao "Tea category classification using morphological characteristics and support vector machines", Proc. SPIE 7126, 28th International Congress on High-Speed Imaging and Photonics, 712613 (10 February 2009); https://doi.org/10.1117/12.821889

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