Improvement and Assessment of Convolutional Neural Network for Tree Species Identification Based on Bark Characteristics

Abstract

Efficient tree species identification is of great importance in forest inventory and management. As the textural properties of tree barks vary less notably as a result of seasonal change than other tree organs, they are more suitable for the identification of tree species using deep learning models. In this study, we adopted the ConvNeXt convolutional neural network to identify 33 tree species using the BarkNetV2 dataset, compared the classification accuracy values of different tree species, and performed visual analysis of the network’s visual features. The results show the following trends: (1) the pre-trained network weights exhibit up to 97.61% classification accuracy for the test set, indicating that the network has high accuracy; (2) the classification accuracy values of more than half of the tree species can reach 98%, while the confidence level of correct identification (probability ratio of true labels) of tree species images is relatively high; and (3) there is a strong correlation between the network’s visual attractiveness and the tree bark’s biological characteristics, which share similarities with humans’ organization of tree species. The method suggested in this study has the potential to increase the efficiency of tree species identification in forest resources surveys and is of considerable value in forest management.