Validation study on the practical accuracy of wood species identification via deep learning from visible microscopic images

HIGHLIGHTS

• What: The aim of this study was to assess the discriminatory capabilities of a CNN for classifying different wood species, irrespective of specific image characteristics. To achieve this objective, this study utilized images with various pixel sizes, enabling a comparison of model accuracy against scenarios in which only images of identical pixel sizes were analyzed. To evaluate the model accuracy, fine-tuning was applied by building on VGG16 with weights derived from ImageNet (ResNet50).
• Who: Marty from the Contact information: a: Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho have published the research: PEER . . .
  

   

  

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