Abstract:
This research investigates the efficacy of various deep learning models for wood type
classification, employing a dataset comprising 2011 images across five distinct wood
classes. Six state-of-the-art architectures, including ResNet-50, DenseNet121,
MobileNetV2, VGG16, VGG19, and Inception-V3, were implemented and evaluated.
Training and model accuracy were assessed to determine each model's performance.
Results reveal that ResNet-50 achieved the highest accuracy, reaching 98%, closely
followed by DenseNet121 and MobileNetV2, both achieving 97%. VGG16 and
Inception-V3 attained accuracies of 94% and 92%, respectively, while VGG19
achieved 92%. These findings underscore the potential of deep learning approaches in
accurately classifying wood types, with ResNet-50 demonstrating superior
performance among the models tested. This study contributes valuable insights into
utilizing deep learning methodologies for wood type classification, offering practical
implications for industries reliant on wood classification for various applications.
