Leveraging Transfer Learning and Synthetic Datasets for Multi-Class Insect Identification

This paper provides a synthetic dataset based on deep learning and transfer learning approach for insect classification. Programmatically created, 10,000 images in all 10 insect classes including bees, beetles, butterflies, cicada, dragonflies, grasshoppers, moths, scorpions, snails, and spiders. Combining segmented bug photos with different backdrops from Pexels, Un-Splash, and Pixabay produced a dataset that was improved even further with COCO annotations to enable exact feature learning. Using transfer learning to achieve outstanding classification accuracy, we updated a CNN model derived from EfficientNet for this purpose. The model was trained to guarantee resilience and prevent overfitting using data augmentation techniques; it obtained 98% accuracy on the test data. Performance criteria including accuracy, recall, F1-score, and a confusion matrix confirmed the usefulness of the model even if minimal misclassifications across related insect species. Offering a scalable replacement for hand data collecting, the results indicate the feasibility of using synthetic datasets for broad insect categorization. This work generates new chances for application of automated insect classification systems in industry, ecology, and environmental monitoring.