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Chinese Journal of Applied Ecology ›› 2023, Vol. 34 ›› Issue (1): 47-57.doi: 10.13287/j.1001-9332.202301.001

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Convolutional neural network tree species identification based on tree-ring radial section image features

GAO Xin1, YANG Li-xin1*, CHEN Zhen-ju1,2,3,4   

  1. 1Tree-Ring Laboratory/Research Station of Liaohe-River Plain Forest Ecosystem CFERN, College of Forestry, Shenyang Agricultural University, Shenyang 110866, China;
    2Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110164, China;
    3Key Laboratory of Desert and Desertification, Chinese Academy of Sciences, Lanzhou 730000, China;
    4National Research Station of Changbai Forest Ecosystem, Er'daobaihe 133613, Jilin, China
  • Received:2022-08-11 Revised:2022-10-11 Online:2023-01-15 Published:2023-06-15

Abstract: Convolutional neural networks can automatically identify tree species based on the images of structural features of tree-rings samples. In this study, we used a tree-ring image dataset for different species to achieve tree-ring based species automatic identification with high accuracy by four convolutional neural network models (LeNet, AlexNet, GoogLeNet, and VGGNet), aiming to determine the identification accuracy of the models, clarify the species misidentification during the automatic processes, and explore the identification differences among the models. The results showed that tree species identification derived from the trained convolutional neural network models was reliable, with the GoogLeNet and LeNet showed the highest (96.7%) and lowest (66.4%) identification accuracy. The tree species identifications using different models were highly consistent. Quercus mongolica and Abies nephrolepis showed the highest (100% for AlexNet) and lowest identification accuracy, respectively. Misidentification could occur among tree species with similar tree-ring structure. The identification accuracy of the models was higher at family and genus levels than that at the species level. The identification accuracy of broadleaf tree species was higher than that of coniferous trees due to distinct radial structure among broadleaf species. Overall, our method achieved a high accuracy for tree species identification, and provided a fast, convenient, and automatic tree species identification by detecting specific tree-ring properties with convolutional neural network.

Key words: tree species identification, tree ring, radial section, convolutional neural network.