Deep features from Pre-trained CNNs and a vision transformer for amazonian timber species recognition using classical machine learning and random image patches

Abstract

Accurate identification of timber species is essential for biodiversity conservation, sustainable forest management, and timber supply chain control. This study evaluated four pre-trained convolutional neural networks—ResNet50, VGG16, InceptionV3, and MobileNetV2 and a Vision Transformer (ViT) as fixed feature extractors for the automated identification of Amazonian timber species. The resulting deep representations were classified using an Artificial Neural Network (ANN), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), and Random Forest (RF). The dataset comprised 864 macroscopic images of wood cross-sections representing six species and five genera from the Brazilian Amazon. Two image-representation strategies were compared: original images and 20 random 224 × 224-pixel patches extracted from each image. Patch-level class probabilities were aggregated at the image level through soft voting, and predictive performance was evaluated using tree-level Leave-One-Group-Out Cross-Validation. The patch-based strategy improved classifier performance and produced more consistent recognition across species, particularly by increasing the recall of classes with the highest error rates when original images were used. Among the evaluated feature extractors, ViT representations yielded the best results when combined with ANN, SVC, and LDA. The ViT + SVC model achieved the highest predictive performance, with 98.29% accuracy and an MCC of 97.96%. ViT + LDA achieved comparable performance, with 97.70% accuracy and an MCC of 97.26%, while requiring substantially less time for hyperparameter optimization. These findings indicate that combining pre-trained deep feature extractors, conventional machine learning algorithms, random patch sampling, and soft-voting aggregation is a promising and computationally efficient strategy for the automated identification of Amazonian timber species.