IDENTIFICAÇÃO DE ESPÉCIES DE PLANTAS UTILIZANDO COMBINAÇÃO DE CLASSIFICADORES

Abstract

The biodiversity of plant species plays a key role in the Earth's ecology, providing food, shelter and maintaining a healthy breathable atmosphere for all living beings. The plants also have medicinal properties and are used for alternative energy sources, such as biofuel. However, the number of plants endangered has gradually increased and the difficulties in the plants manual recognition process, does become a complex and slow task. A viable method for the identification of plants, or to provide a categorization of the plant, is the plant image acquisition and use pattern recognition techniques. In this way, the use of computers, despite having little contribution in the area, can provide important information on the taxonomy of plants, and can serve as a basis for systems that perform tasks such as the selection of certain plants or to guide the specialist for possible decision-making. This paper proposes a method for classification of plants based on collaborative images of the world experts. This method is able to deal with some complexities imposed during the capture of images, as the presence of noise (lighting, shadows and undesirable objects) and plants position variations. To accomplish this task are used texture descriptors based on SIFT, SURF and HOG, which have shown excellent results in several works. To enable testing of the proposed method, we used an image provided by the global task basis for recognition of plants in 2011, ImageCLEF, containing about 2,586 plant samples composed by 41 species divided into two distinct categories: the first one with 13 species and images with presence of noise, and with the second species and 28 sheets of images plotted on a white background. The results of the experiments show that the classifiers trained with texture descriptors are able to achieve good hit rates close to 70%, given the complexity of the problem. Classifiers combination methods have also been used and have been shown capable to improve the performance of classifiers, especially in the test with images that has the presence of noises.