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Background: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 commercially available Brazilian wood species using a custom DCNN model. Results: Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model’s robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion: The custom machine learned model accuracy was higher than 0.90. The model’s worst performance was identified in distinguishing between Toona ciliate and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security.

The productive sustainability of eucalyptus planting is threatened when both diseases and disorders of growth occur. These interferences can have abiotic origin as environmental conditions can negatively affect the wood quality. This study has the goal of evaluating influence of different levels of physiological disorders in dendrometric variables and wood quality from seven-year-old Eucalyptus grandis x Eucalyptus urophylla hybrids intended for wood pulping production. The trees from commercial plantations in State of Maranhão, Brazil were separated into three levels of physiological disorders by means of morphologic changes. Then, they were evaluated according to dendrometric variables, anatomical characteristics, chemical properties, density of wood, and kraft pulp process. The physiological disorder levels significantly influenced all evaluated properties. Moreover, the wood from trees with higher severity of symptoms resulted in higher average values of dendrometric variables, basic density, and fibers dimensions. In addition, apparent density exhibited a different standard in the pith region with increase on radial wood profile. Trees with the least severe disorders exhibited changes in dendrometric and anatomical variables, mainly in wood volume and fibers dimensions, respectively.