Bird Species Classification: Using CNN Models

This research employs Convolutional Neural Networks (CNNs) to identify bird species. A dataset of 1701 curated images, featuring Abbotts booby, Abbotts babbler, Abyssinian ground hornbill, African crowned crane, African firefinch, African oyster catcher, African pied hornbill, African pygmy goose, and albatross forms a comprehensive image library. Utilizing deep learning architectures (ResNet, VGG16, VGG19, AlexNet, GoogleNet, LeNet), the trained neural networks achieved remarkable accuracies, with some models exceeding 99%, demonstrating robust bird species classification. The study's 91% to over 99% accuracy rate in identifying different bird species emphasizes its usefulness in real-world situations. The fields of computer vision and ornithology benefit from this research, which helps classify bird species. Offering a thorough description of the model's efficacy and possible implications for conservation monitoring tools or apps for identifying bird species, the study describes the experimental design, evaluation measures, dataset characteristics, and concluding thoughts