The study explores how different deep learning approaches perform in identifying tropical wood species from macroscopic images. In the study, non-optimized, transfer learning applied, and optimized convolutional neural network (CNN) models were compared. The obtained results show that the optimized models, featuring EfficientNetV2B3, exhibit extremely high accuracy and performance in tropical wood classification. In the evaluation of the optimized models, EfficientNetV2B3 achieved the highest performance with 99.01% accuracy, 99.02% precision, 99.01% recall, and F1-score values. Xception and MobileNetV2 also gave impressive results with 98.64% and 98.02% accuracy, respectively. These results reveal that the optimized 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 optimized. In conclusion, this study recommends the use of the EfficientNetV2B3 model for the classification of tropical wood species and emphasizes that this model constitutes a reference point in this field with its high accuracy, sensitivity, and generalization 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.