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Related Experiment Video

Updated: Aug 9, 2025

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

Xin Gao1, Li-Xin Yang1, Zhen-Ju Chen1,2,3,4

  • 1Tree-Ring Laboratory/Research Station of Liaohe-River Plain Forest Ecosystem CFERN, College of Forestry, Shenyang Agricultural University, Shenyang 110866, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|February 17, 2023
PubMed
Summary
This summary is machine-generated.

Convolutional neural networks accurately identify tree species using tree-ring images. GoogLeNet achieved 96.7% accuracy, demonstrating reliable automated tree identification.

Keywords:
convolutional neural networkradial sectiontree ringtree species identification

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Area of Science:

  • Botany
  • Computer Science
  • Machine Learning

Background:

  • Automated tree species identification is crucial for forestry and ecological studies.
  • Traditional methods often rely on manual analysis of tree-ring structures, which can be time-consuming and subjective.
  • Convolutional neural networks (CNNs) offer a promising approach for analyzing complex image data, including tree-ring features.

Purpose of the Study:

  • To evaluate the accuracy of four CNN models (LeNet, AlexNet, GoogLeNet, VGGNet) for automated tree species identification using tree-ring images.
  • To identify patterns of species misidentification and compare the performance differences among the CNN models.
  • To explore the influence of taxonomic levels (species, genus, family) and tree types (broadleaf, coniferous) on identification accuracy.

Main Methods:

  • A dataset of tree-ring images from various species was compiled.
  • Four CNN models were trained and tested on the dataset for species identification.
  • Identification accuracy, misidentification patterns, and model performance variations were analyzed.

Main Results:

  • CNN models provided reliable tree species identification, with GoogLeNet achieving the highest accuracy (96.7%) and LeNet the lowest (66.4%).
  • Species with similar tree-ring structures were more prone to misidentification.
  • Identification accuracy was higher at the genus and family levels compared to the species level.
  • Broadleaf tree species identification accuracy surpassed that of coniferous trees due to more distinct radial structures.

Conclusions:

  • CNNs are effective tools for accurate and automated tree species identification based on tree-ring morphology.
  • The study highlights the potential for CNNs to revolutionize forestry inventory and ecological research.
  • Further research can optimize CNN architectures and datasets for even higher identification precision across diverse tree species.