Plant species recognition by leaf texture analysis

Dalcimar Casanova

Instituto de Ciências Matemáticas e de Computação - Universidade de São Paulo, 2008

Biodiversity of species existing in the plant kingdom make the use of traditional models of taxonomy, a process of classification traditionally performed manually, a very complex and time-consuming task. Most of difficulties in that process result from the existence of few researches on plant classification using mathematical and computational methods. In this way, to contribute with the taxonomy techniques already developed, this study aims to develop and test a computational method for identifying plant species by leaf texture analysis. Motivated by the TreeVis project, this work is a comprehensive revision of texture analysis methods used in digital images (focus concentrated in features extraction and classification). This study investigates the applicability of traditional methods such as co-occurrence matrix, state of the art techniques as Gabor wavelets, and new and promising texture analysis methods, such as volumetric fractal dimension. In classification context is investigated methods of pattern recognition based on multivariate data analysis, artificial neural networks and committee machines. Although leaf classes present high similarity between classes and not appropriate similarity intraclasses, the results obtained are excellent. The best strategy for classification, using committee machines with descriptors of Gabor wavelets/color and volumetric fractal dimension/color, yielded a high probability of success, 96:32% in 40 classes studied. This result demonstrates how computational methods of images analysis, in particular texture analysis, can contribute and make more easier and faster the task of identifying plant species

keywords: Computer vision,Leaf,Pattern recognition,Taxonomy,Texture analysis,Vegetal identification