Tree-Ring Laboratory/Research Station of Liaohe-River Plain Forest Ecosystem CFERN, College of Forestry, Shenyang Agricultural University, Shenyang 110866, China.
Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110164, China.
Ying Yong Sheng Tai Xue Bao. 2023 Jan;34(1):47-57. doi: 10.13287/j.1001-9332.202301.001.
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. and 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.
卷积神经网络可以根据树木年轮样本的结构特征图像自动识别树种。本研究使用树木年轮图像数据集对不同树种进行分析，通过四个卷积神经网络模型（LeNet、AlexNet、GoogLeNet 和 VGGNet）实现高精度的基于树木年轮的自动树种识别，旨在确定模型的识别准确性、明确自动识别过程中的物种误识别情况，并探讨模型之间的识别差异。结果表明，通过训练的卷积神经网络模型进行树种识别是可靠的，GoogLeNet 和 LeNet 的识别准确率最高（96.7%）和最低（66.4%）。不同模型的树种识别结果高度一致，AlexNet 的准确率最高（100%），而 VGGNet 的准确率最低（100%）。具有相似年轮结构的树种之间可能会发生误识别。与物种水平相比，模型在科和属水平上的识别准确率更高。阔叶树种的识别准确率高于针叶树种，这是因为阔叶树种的径向结构明显不同。总的来说，我们的方法实现了高精度的树种识别，为通过检测树木年轮特征提供了快速、便捷和自动的树种识别方法。
