In this work we address the problem of forest species recognition which is a very challenging task and has several potential applications in the wood industry. The first contribution of this work is a database composed of 22 different species of the Brazilian flora that has been carefully labeled by expert in wood anatomy. In addition, in this work we demonstrate through a series of comprehensive experiments that color-based features are quite useful to increase the discrimination power for this kind of application. Last but not least, we propose a segmentation approach so that a wood can be locally processed to mitigate the intra-class variability featured in some classes. Such an approach also brings important contribution to improve the final performance in terms of classification.

The application of artificial intelligence (AI) techniques has improved the accuracy of forest species identification, particularly in timber inventories conducted under Sustainable Forest Management (SFM). This study developed and evaluated machine learning models to recognize 16 Amazonian timber species using digital images of tree bark. Data were collected from three SFM units located in Nova Maringá, Feliz Natal, and Cotriguaçu, in the state of Mato Grosso, Brazil. High-resolution images were processed into sub-images (256 × 256 pixels), and two feature extraction methods were tested: Local Binary Patterns (LBP) and pre-trained Convolutional Neural Networks (ResNet50, VGG16, InceptionV3, MobileNetV2). Four classifiers—Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Linear Discriminant Analysis (LDA)—were used. The best result (95% accuracy) was achieved using ResNet50 with SVM, confirming the effectiveness of transfer learning for species recognition based on bark texture. These findings highlight the potential of AI-based tools to enhance accuracy in forest inventories and support decision-making in tropical forest management.

Accurate identification of forest species from wood samples is essential for combating illegal logging and supporting sustainable wood trade. This study benchmarks 15 deep learning models from three YOLO generations (YOLOv8, YOLO11, and YOLO26), each in five sizes (nano through extra-large), to classify 73 forest species from macroscopic wood images. A dataset of 77,865 patches (448 \(\times\) 448 pixels) was assembled from three heterogeneous image capture protocols with spatial resolution normalization. Four models achieved a test accuracy of 99.87% (MCC = 0.9987), with YOLO26-medium offering the best efficiency at 232.5 frames per second. All 15 models reached 100% Top-3 accuracy, and error analysis revealed that misclassifications were confined to individual patches. Image-level majority voting across patches yielded 100% accuracy for YOLO26-medium on test images. Multi-scale RISE saliency maps confirmed that the models attend to anatomically relevant features, such as pore arrangements and parenchyma patterns, while t-SNE analysis showed that YOLO26 produces the best-separated feature spaces. An out-of-distribution detection evaluation using three complementary methods (Maximum Softmax Probability, energy-based scoring, and Mahalanobis distance) confirmed strong model discriminability against both general out-of-domain content and unseen wood species acquired under the same protocols. These results establish a new benchmark for macroscopic wood species identification and suggest that medium-sized YOLO models are promising candidates for future deployment on resource-constrained devices for field inspection.