Deep Learning methodology for the identification of wood species using high-resolution macroscopic images

Significant advancements in the field of wood species recognition are needed worldwide to support sustainable timber trade. In this work we contribute to automate the identification of wood species using machine learning techniques and high-resolution macroscopic images of timber. The main challenge of this problem is that fine-grained patterns in timber are crucial in order to accurately identify wood species, and these patterns are not properly learned by traditional convolutional neural networks (CNNs) trained on low/medium resolution images. To this end, in the context of funded project GoIMAI, we have collected a Wood Species dataset to cover 37 wood species. Most of them CITES-listed and some similar species of the same genera. We propose a patch-based classification with a voting mechanism that overcomes the inherent challenges that CNNs face in timber identification. Our method is able to capture fine-grained patterns in timber and boost robustness and prediction accuracy of the model. Our experiments have assessed the performance of the method, involving the comparison of several model architectures proposed in the literature, the analysis of the parameters of our proposed methodology, and the exploration of data augmentation methods.