Automatic Classification of Wood Species Using Deep Learning

Wood identification can be done by image recognition or classification. The main idea is that the proposed model is independent in terms of number of input features and / or images. We use a texture analysis and convolutional neural network. The number of source classes depend on the selection and availability of a sufficient (and equal) number of representative samples for each class used in the training phase. We build a model, which perform a reliable classification of tree types of species: Tropical, Diffuse Porous and Ring Porous, obtaining 89.94% accuracy.

• Nicole Christoff
• Nikolai Bardarov
• Dessislava Nikolova

2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

https://ieeexplore.ieee.org/document/9894170