Automating bird species classification: A deep learning approach with CNNs

Abstract

Bird species identification and classification are challenging yet crucial for research and conservation. Traditional methods are labor-intensive, require specialized expertise, and can be prone to error. Recent advances in deep learning offer an automated solution to this complex problem. This study evaluates a convolutional neural network (CNN) model for classifying images of 525 bird species. The model employs transfer learning using the EfficientNetB0 architecture and was trained on over 84,000 images. With data augmentation, the model achieved 87% validation accuracy and 86.7% test accuracy, demonstrating its ability to overcome limited data and generalize well. Difficulties in obtaining balanced, high-quality data for each species were addressed through transfer learning and augmentation.