Wood species automatic identification from wood core images with a residual convolutional neural network.

• A fully automatic approach for tree species identification is proposed. • The method is dedicated to images of scanned wood cores. • The method employs a residual convolutional encoder network in a sliding window setup. • The method was tested on a dataset of images representing 14 European tree species. • Tree wood species of about 99% core samples were identified correctly. This paper tackles the problem of automatic tree species identification from scanned images of wood cores. A convolutional neural network with residual connections is proposed to perform this task. The model is applied to consecutive image patches following the sliding window strategy to recognize a patch central pixel's membership. It then decides about the resulting tree species via a majority voting. The model's performance was assessed concerning a dataset of 312 wood core images representing 14 European tree species, including both conifer and angiosperm (ring-porous and diffuse-porous) wood. Two tasks were considered, including wood patch classification and wood core classification. In these tasks, the proposed model correctly recognized species of almost 93% the wood image patches and 98.7% of wood core images. It also outperformed the state-of-the-art convolutional neural network-based competitor by 9% and 3%, respectively. The influence of the model's parameters and training set-up on its performance is analyzed in the manuscript to ensure the highest recognition rates of wood species. The source code of the proposed method is released together with the corresponding image dataset to facilitate the reproduction of results. [ABSTRACT FROM AUTHOR]