Live Breaking News & Updates on Forest Species Database Macroscopic

Tree species identification is critical to support their conservation, sustainable management and, particularly, the fight against illegal logging. Therefore, it is very important to develop fast and accurate identification systems even for non-experts. In this research we have achieved three main results. First, we developed from scratch and using new sample collecting and processing protocols an dataset called CRTreeCuts that comprises macroscopic cross-section images of 147 native tree species from Costa Rica. Secondly, we implemented a CNN for automated tree species identification based on macroscopic images of cross-sections of wood. For this CNN we apply the fine-tuning technique with VGG16 as a base model, pre-trained with the ImageNet data set. This model is trained and tested with a subset of 75 species from CRTreeCuts. The top-1 and top-3 accuracies achieved in the testing phase are 70.5% and 80.3%, respectively. The Same-Specimen-Picture Bias (SSPB), which is known to er ....

Sumatera Utara , United States , Costa Rica , Sulawesi Selatan , Provincia De Puntarenas , San Josét , Costa Rican , Rojas Xylotheque , Xylarium Bogoriense , Servicios Ambientales , Fournier Origgi , Imagem Filho , Costa Rica Institute Of Technology , Convolutional Neural Network Architecture , Inverted Residual Mottenecks Blocks , Forest Species Database Macroscopic , Verly Lopes , Same Specimen Picture Bias , Picture Bias , Forest Species Database , Costa Rica Institute , Pacific Coast , Experimental Horizontes , Nacional La Cangreja , Core Processor , Bionic Framework ,