Progressive Image Resizing for wood Species Classification from Macroscopic Images | ||||
| Fayoum University Journal of Engineering | ||||
| Volume 8, Issue 1, January 2025, Page 1-15 PDF (936.22 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/fuje.2024.276630.1070 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Fatma Mazen Ali Mazen 1; Ahmed Aly Nashat  2; Marian Mamdouh Ibrahim3
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| 1Fayoum University - Faculty of Eng. - Dept. of Electronics and Communication Eng. Fayoum 63514 | ||||
| 2Fayoum University Faculty of Engineering Electronics and Communication Engineering Dept. | ||||
| 3Electrical Engineering Dept., Ahram Canadian University, 6 October City, Giza, Egypt | ||||
| Abstract | ||||
| Wood is an important raw material used in various activities, such as building, furniture, and fuel. The timber industry is significant in many countries and has a significant financial impact. There are diverse categories of wood, each with its unique properties, and experts typically perform wood species identification through visual inspection, which is a tedious and time-consuming process. To eliminate the need for manual detection, a deep learning-based wood Classifica-tion System was proposed in this paper. The system uses a transfer learn-ing-based convolutional neural network model that handles feature extraction. Compared to other transfer learning models such as VGG16, ResNet50, and DenseNet201, the proposed EfficientNetB7 model achieved a high validation accuracy of 99.824%, which suggests that it can be used to aid unskilled agents in wood categorization. This new strategy can save time and effort in the identi-fication of wood species, making it an efficient method for the timber industry. | ||||
| Keywords | ||||
| wood classification; progressive resizing; Wood Species dataset; differential learning rates; EfficientNet, ResNet, and DenseNet | ||||
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