Amazon wood species classification: a comparison between deep learning and pre-designed features

In many countries, the wood industry is a crucial sector and has a significant economic impact. In this sense, illegal logging is a way to reduce costs, avoiding taxes, or having access to more valuable wood species. To combat the latter, the recognition of wood species is crucial. However, this task is usually performed by experts through visual inspection, a process that requires sanding and cleaning the wood surface, and an impractical task for use in the field. In this paper, the acquisition process was simplified and a new wood dataset was introduced, where a simple pocket knife cut is used to expose the timber section for inspection. Four deep learning models with transfer learning were investigated and compared with traditional pre-designed feature methods. Additionally, the models were evaluated with a cross-validation scheme to avoid any bias. The experimental results show that deep learning outperforms pre-design features for wood classification. DenseNet achieved 98.13% of accuracy, indicating that it could be applied to assist untrained agents in wood classification.