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Using Deep Learning to Identify Costa Rican Native Tree Species From Wood Cut Images.

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Automated tree species identification using CNNs aids conservation and combats illegal logging. A new dataset and mobile app enable accurate identification from wood cross-sections.

Keywords:
automated image-based tree species identificationconvolutional neural networkcosta rican tree speciesdeep learningplant classificationxylotheques

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Area of Science:

  • Botany
  • Computer Science
  • Conservation Science

Background:

  • Accurate tree species identification is crucial for conservation efforts and combating illegal logging.
  • Developing accessible tools for rapid identification is essential for non-experts.

Purpose of the Study:

  • To create a dataset of Costa Rican tree species cross-sections.
  • To develop a Convolutional Neural Network (CNN) for automated tree species identification.
  • To build a mobile application for on-site tree identification.

Main Methods:

  • Developed the CRTreeCuts dataset with macroscopic cross-section images of 147 native Costa Rican tree species.
  • Implemented a fine-tuned VGG16 CNN model for species identification using wood cross-section images.
  • Trained and tested the CNN on a subset of the CRTreeCuts dataset, ensuring absence of Same-Specimen-Picture Bias (SSPB).

Main Results:

  • Achieved top-1 accuracy of 70.5% and top-3 accuracy of 80.3% in tree species identification.
  • Validated the absence of SSPB in all experimental runs, ensuring reliable model performance.
  • Developed the Cocobolo Android application for practical, automated tree species identification.

Conclusions:

  • The developed CNN and CRTreeCuts dataset offer a robust solution for automated tree species identification.
  • The Cocobolo mobile app provides a user-friendly tool for conservationists and forest managers.
  • This technology supports sustainable forest management and the fight against illegal logging.