title: Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques.
abstract: Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.
authors.affiliation: College of Chemistry and Life Science, Zhejiang Normal University, Jinhua, China. luhongfei63@yahoo.com.cn
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lu
authors.forename: Wu
authors.initials: W
authors.lastname: Jiang
authors.forename: M
authors.initials: M
authors.lastname: Ghiassi
authors.forename: Sean
authors.initials: S
authors.lastname: Lee
authors.forename: Mantri
authors.initials: M
authors.lastname: Nitin
mesh_terms: D028244:Camellia / Q000033:anatomy & histology* / Q000145:classification*; D016000:Cluster Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D018515:Plant Leaves / Q000033:anatomy & histology* / Q000145:classification*