Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

Abstract

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

Mask Region-Based Convolutional Neural Network (R-CNN) Based Image Segmentation of Rays in Softwoods

“…Deep learning is a part of machine learning that enables automatic measurement, a vast amount of data acquisition and analysis through learning. Therefore, deep learning has the potential to automatically identify wood species efficiently (de Geus et al, 2021;Fabijańska et al, 2021;Fathurahman et al, 2021;Wu et al, 2021).…”