Enhanced Bird Species Identification using ResNet-50: A Deep Learning Framework for High-Performance Classification

Bird species identification is a critical task in ornithology, ecology, and environmental conservation, providing essential data for biodiversity monitoring and ecological research. Traditional methods relying on expert knowledge and manual identification are not scalable to the vast image datasets generated by modern technologies. This study explores the application of the ResNet-50 deep convolutional neural network model for the identification of bird species using a highly curated dataset consisting of 525 bird species with a total of 89,885 images. The dataset was split into 80% for training and 20% for testing, ensuring a rigorous evaluation of the model’s performance. The ResNet-50 model, known for its deep architecture and residual learning capabilities, was fine-tuned to achieve optimal results in this classification task. The findings reveal that the ResNet-50 model achieved an impressive accuracy of 95.80%, a precision of 95.60%, a recall of 95.40%, and an F1-Score of 95.50%. These metrics reflect the model’s robustness and effectiveness in accurately identifying bird species, even in the presence of significant imbalances in the dataset, such as the male-to-female ratio. The study underscores the potential of deep learning models like ResNet-50 in ecological applications, particularly for biodiversity monitoring and conservation efforts. Future research could focus on incorporating additional data modalities and expanding the dataset to improve the model’s generalizability and applicability to a broader range of species and environmental conditions.