Akademik Veri Yönetim Sistemi
DREWNO, cilt.0, sa.0, ss.1-18, 2026 (SCI-Expanded, Scopus)
The study examines the performance of various deep learning approaches in identifying tropical wood species from macroscopic images. In the study, non-optimised, transfer learning applied, and optimised convolutional neural network (CNN) models were compared. The obtained results show that the optimised models, featuring EfficientNetV2B3, exhibit remarkably high accuracy and performance in tropical wood classification. In the evaluation of the optimised models, EfficientNetV2B3 achieved the highest performance with 99.01% accuracy, 99.02% precision, 99.01% recall, and F1-score values. Xception and MobileNetV2 also achieved notable results with 98.64% and 98.02% accuracy, respectively. These results reveal that the optimised models, especially EfficientNetV2B3, are highly effective for tropical wood classification. Compared with the literature, this study has made significant progress in the field of wood species classification, especially by achieving an accuracy rate of 99.01% with the EfficientNetV2B3 model. These results demonstrate how effective deep learning models can be on complex classification problems, especially when they are optimised. In conclusion, this study recommends the use of the EfficientNetV2B3 model for the classification of tropical wood species and emphasises that this model serves as a benchmark in this field with its high accuracy, precision, and generalisation ability. In the future, it is suggested to further develop this method by testing it on different datasets and classification problems. This work provides a significant contribution to the fields of wood science and automatic species recognition.