A bark recognition algorithm for plant classification using a least square support vector machine

Blaanco, Luis J.; Travieso, Carlos M.; Quinteiro, José Ma; Hernández, Pastora Calvo; Dutta, Malay Kishore; Singh, Anushikha

doi:10.1109/ic3.2016.7880233

Cited by 6 publications

(1 citation statement)

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“…Early studies of tree species identification usually used artificial neural networks or support vector machines to analyze the hyper-spectral features of trees, and there were also multiple methods that used texture feature extraction and descriptors to assist in tree classification [4][5][6][7][8][9][10][11][12][13]. Although these methods reduced some manual costs of classification, traditional machine learning methods usually required human design or selection of texture features and descriptors, which were subjective and often could not fully express the complexity and diversity of textures, and lost some information during post-processing operations, such as dimensionality reduction [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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

Cui

et al. 2023

Forests

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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.

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