LAUSR

Advances in species recognition technologies can contribute to the conservation and protection of flora species, especially those threatened with extinction. The aim of this research was to compare the early fusion approaches of operators known as Local Binary Patterns (LBP) and late fusion, carried out at the level of the decision classifiers, in the construction of an automatic recognition system of forest species. 1901 macroscopic images of wood from 46 Brazilian species were used. The extraction of image characteristics was done using two variants of the LBP descriptor, covering different aspects of spatial and angular resolution. The repeated stratified k-fold cross-validation method was used to estimate the performance of the classifiers. The cross-validation folds were created using stratified random sampling, whose strata were the prediction classes. An automatic recognition system based on the concatenation of rotation-invariant LBP histograms and the SVM classifier showed an F1-score of 97.67%. The fusion of classifiers, through majority voting, improved the F1-score of this system by 0.33% point. This experiment revealed that more than 50% of the species showed no misclassification or occurred only once or twice. It was identified that some groups of species generally confused by wood anatomists were perfectly differentiated by this classification system. The recognition system showed good ability to identify species, and if this technology is combined with traditional identification tools and empirical knowledge, it is possible to minimize errors in the identification of Brazilian flora, especially endangered species, for which the proposed classification system showed high accuracy.