Enhanced Approaches to Kingfisher Species Identification Using Machine Learning and Deep Learning

This study addresses the critical challenge of bird species recognition within the domains of deep learning, emphasizing its significant ecological and conservation implications. Focusing on Sri Lanka’s avian biodiversity, particularly the Kingfisher family, this research systematically analyzes bird identification methodologies. The methodology encompasses data collection, pre-processing, feature extraction, classification, and analysis of results. The study employs various deep learning models, including VGG16, VGG19, SVM, a custom CNN, and a hybrid CNN-SVM model. The custom CNN model, specifically optimized for kingfisher species identification, achieved a testing accuracy of 81.21%, outperforming traditional models. However, the hybrid CNN-SVM model demonstrated slightly lower testing accuracy at 79.35%. These findings highlight the effectiveness of the proposed deep learning models in identifying Kingfisher species, particularly when dealing with complex variations in plumage and environmental factors. The results indicate that the custom CNN model is highly reliable for ornithological applications, especially for nonspecialists. The research highlights the complexity of Kingfisher species identification and underscores the critical role of advanced machine learning techniques in addressing these challenges. The findings have significant implications for wildlife conservation, enabling enhanced monitoring and preservation of bird populations. Additionally, the results reveal the potential for developing a mobile application for bird species recognition, particularly for Kingfishers, addressing the lack of existing computer-based identification solutions.