  Significant advancements in the field of wood species identification are
needed worldwide to support sustainable timber trade. In this work we
contribute to automate the identification of wood species via 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.
  We propose a Timber Deep Learning Identification with Patch-based Inference
Voting methodology, abbreviated TDLI-PIV methodology. Our proposal exploits the
concept of patching and the availability of high-resolution macroscopic images
of timber in order to overcome the inherent challenges that CNNs face in timber
identification. The TDLI-PIV methodology is able to capture fine-grained
patterns in timber and, moreover, boosts robustness and prediction accuracy via
a collaborative voting inference process.
  In this work we also introduce a new data set of marcroscopic images of
timber, called GOIMAI-Phase-I, which has been obtained using optical
magnification in order to capture fine-grained details, which contrasts to the
other datasets that are publicly available. More concretely, images in
GOIMAI-Phase-I are taken with a smartphone with a 24x magnifying lens attached
to the camera. Our data set contains 2120 images of timber and covers 37
legally protected wood species.
  Our experiments have assessed the performance of the TDLI-PIV methodology,
involving the comparison with other methodologies available in the literature,
exploration of data augmentation methods and the effect that the dataset size
has on the accuracy of TDLI-PIV.
