Camellia (Theaceae) classification with support vector machines based on fractal parameters and Red, Green, and Blue intensity of leaves

W. Jiang, Z. M. Tao, Z G. Wu, N. Mantri, H. F. Lu, Z. S. Liang


Leaf traits are commonly used in plant taxonomic applications. The aim of this study was to test the utility of fractal leaf parameters analysis (FA) and leaf red, green, and blue (RGB) intensity values based on support vector machines as a method for accurately discriminating Camellia (68 species from five sections, 11 from sect. Furfuracea, 13 from sect. Paracamellia, 15 from sect. Tuberculata, 24 from sect. Theopsis and 5 from sect. Camellia). The results showed that the best classification accuracy as up to 96.88% using the RBF SVM classifier (C = 16, g = 0.5). The linear kernel overall accuracy was 90.63%, and the correct classification rates of 40.63% and 93.75% were achieved for the sigmoid SVM classifier (C = 16, g = 0.5) and the polynomial SVM classifier (C = 16, g = 0.5, d = 2), respectively. A hierarchical dendrogram based on leaf FA and RGB intensity values was mostly on agreement with the generally accepted classification of the Camellia species. SVM combined with FA and RGB may be used for rapidly and accurately classifying Camellia species and identifying unknown genotypes.

Keywords: Camellia; Classification; Fractal analysis; RGB; SVM.

Bangladesh J. Plant Taxon. 24(1): 6581, 2017 (June)

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