Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

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.

叶片性状已成功应用于山茶属（Camellia，山茶科Theaceae）物种的分类，但此前尚未有研究将叶片性状与监督式模式识别技术相结合展开探索。本研究利用山茶属5个组共计93个物种的叶片形态与叶脉性状，评估多种监督式模式识别技术在分类任务中的效能并对比其分类精度。本研究采用聚类方法、学习向量量化神经网络（Learning Vector Quantization neural network，LVQ-ANN）、人工神经网络动态架构（Dynamic Architecture for Artificial Neural Networks，DAN2）以及C-支持向量机（C-support vector machines，SVM），对山茶属5个组的93个物种进行分类鉴别：其中糙果茶组（sect. Furfuracea）11种、短柱茶组（sect. Paracamellia）16种、瘤果茶组（sect. Tuberculata）12种、山茶组（sect. Camellia）34种、茶组（sect. Theopsis）20种。DAN2与SVM在山茶属物种分类中表现优异，其中DAN2在训练集与测试集上的分类精度分别为97.92%与91.11%。径向基核函数支持向量机（RBF-SVM）在训练集与测试集上的精度分别为97.92%与97.78%，取得了最优的分类效果。基于叶片结构数据构建的层级聚类树状图，验证了此前提出的山茶属5个组的形态学分类方案。综合研究结果表明，采用监督式模式识别技术对叶片结构数据进行分析，尤其是结合DAN2与SVM分类方法，可高效准确地鉴别山茶属物种。