Biometric identification is a pattern recognition based classification system that recognizes an individual by determining its authenticity using a specific physiological or behavioural characteristic (biometric). In contrast to number of commercially available biometric systems for human recognition in the market today, there is no such a biometric system for plant recognition, even though they have many characteristics that are uniquely identifiable at a species level. The goal of the study was to develop a plant species biometric using both global and local features of leaf images. In recent years, various approaches have been proposed for characterizing leaf images. Most of them were based on a global representation of leaf peripheral with Fourier descriptors, polygonal approximations and centroid-contour distance curve. Global representation of leaf shapes does not provide enough information to characterise species uniquely since different species of plants have similar leaf shapes. Others were based on leaf vein extraction using intensity histograms and trained artificial neural network classifiers. Leaf venation extraction is not always possible since it is not always visible in photographic images. This study proposed a novel approach of leaf identification based on feature hierarchies. First, leaves were sorted by their overall shape using shape signatures. Then this sorted list was pruned based on global and local shape descriptors. The consequent biometric was tested using a corpus of 200 leaves from 40 common New Zealand broadleaf plant species which encompass all categories of local information of leaf peripherals. Two novel shape signatures (full-width to length ratio distribution and half-width to length ratio distribution) were proposed and biometric vectors were constructed using both novel shape signatures, complex-coordinates and centroid-distance for comparison. Retrievals were compared and the biometric vector based on full-width to length ratio distribution was found to be the best classifier. Three types of local information of the leaf peripheral (leaf margin coarseness, stem length to blade length ratio and leaf tip curvature) and the global shape descriptor, leaf compactness, were used to prune the list further. The proposed biometric was able to successfully identify the correct species for 37 test images (out of 40). The proposed biometric identified all the test images (100%) correctly if two species were returned compared to the low recall rates of Wang et al. (2003) (30%, if 10 images were returned) and Ye et al. (2004) (71.4%, if top 5 images were returned). The biometric can be strengthened by adding reference images of new species to the database, or by adding more reference images of existing species when the reference images are not enough to cover the leaf shapes.

A diversidade de espécies presentes no riquíssimo reino vegetal torna o processo de identificação de órgãos foliares uma tarefa muito complexa. A biodiversidade das espécies, associada aos modelos tradicionais de taxonomia, transforma essa tarefa em um verdadeiro desafio para os pesquisadores. Neste trabalho é apresentada uma nova abordagem para identificação de espécies vegetais baseada em características internas dos órgãos foliares. A coleta de informações é realizada através de técnicas de visão computacional e análise de imagens, através das quais são extraídas características relativas à complexidade (dimensão fractal) e biometria dos órgãos foliares. A eficiência da metodologia desenvolvida foi avaliada em casos reais de identificação de espécies, em que foram utilizados dois conjuntos de imagens: espécies da Mata Atlântica e do Cerrado brasileiro, e espécies de maracujás silvestres do gênero Passiflora. Para classificação das espécies foram utilizadas as técnicas de reconhecimento padrões de análise de agrupamentos e redes neurais artificiais. / The plant species diversity makes their correct identification a very complex task. The traditional taxonomy models, associated with species biodiversity, has been transformed this task in a challenger for the researches. This work presents a new approach to plant species identification, based on internal characteristics of leaf form. The data are collected by computer vision and shape analysis techniques, which extracts features from complexity (fractal dimension) and biometry of plant species. The methodology efficiency was evaluated with real cases of species identification: digital images of Mata Atlântica and brazilian Cerrado species; and passion fruit species of genus Passiflora. The species classifications are performed using pattern recognition techniques as clustering and artificial neural networks.

A diversidade de espécies presentes no riquíssimo reino vegetal torna o processo de identificação de órgãos foliares uma tarefa muito complexa. A biodiversidade das espécies, associada aos modelos tradicionais de taxonomia, transforma essa tarefa em um verdadeiro desafio para os pesquisadores. Neste trabalho é apresentada uma nova abordagem para identificação de espécies vegetais baseada em características internas dos órgãos foliares. A coleta de informações é realizada através de técnicas de visão computacional e análise de imagens, através das quais são extraídas características relativas à complexidade (dimensão fractal) e biometria dos órgãos foliares. A eficiência da metodologia desenvolvida foi avaliada em casos reais de identificação de espécies, em que foram utilizados dois conjuntos de imagens: espécies da Mata Atlântica e do Cerrado brasileiro, e espécies de maracujás silvestres do gênero Passiflora. Para classificação das espécies foram utilizadas as técnicas de reconhecimento padrões de análise de agrupamentos e redes neurais artificiais. / The plant species diversity makes their correct identification a very complex task. The traditional taxonomy models, associated with species biodiversity, has been transformed this task in a challenger for the researches. This work presents a new approach to plant species identification, based on internal characteristics of leaf form. The data are collected by computer vision and shape analysis techniques, which extracts features from complexity (fractal dimension) and biometry of plant species. The methodology efficiency was evaluated with real cases of species identification: digital images of Mata Atlântica and brazilian Cerrado species; and passion fruit species of genus Passiflora. The species classifications are performed using pattern recognition techniques as clustering and artificial neural networks.