<![CDATA[Background: Accurate butterfly species classification plays an important role in biodiversity monitoring and environmental conservation. However, image-based classification remains challenging due to similarities in wing color patterns and shapes among species. Aims: This study aims to evaluate the performance of butterfly image classification using transfer learning based on the EfficientNet architecture with different optimization strategies. Methods: EfficientNet-B2 and EfficientNet-B3 were implemented as the main models. Image preprocessing techniques, including resizing, normalization, and data augmentation, were applied to improve model performance. The models were optimized using Adam and AdamW optimizers with different learning rates. Performance evaluation was conducted using accuracy, precision, recall, and F1-score. Results: The results show that EfficientNet-B2 optimized with the Adam optimizer (learning rate 5 × 10⁻⁴) achieved the best performance, with a validation accuracy of 92.62% and an F1-score of 92.61%. In comparison, EfficientNet-B3 optimized using AdamW (learning rate 6 × 10⁻⁴) produced slightly lower performance, indicating the influence of optimizer selection and learning rate configuration on model convergence and generalization. Conclusion: EfficientNet-B2 combined with the Adam optimizer provides a stable and effective approach for butterfly species classification. These findings highlight the importance of optimization strategies in improving model performance and support the development of automated systems for biodiversity monitoring.]]>
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