An Eagle-Eye Vision: Advancements in Avian Species Classification

Avian species taxonomy is an important activity in the domain of ornithology, wildlife preservation, and ecological research. Convolutional Neural Networks (CNNs) a widely used tool for automating categorization jobs in recent years. This abstract provides an overview of applying Eagle-Eye CNN to the classification of bird species. The methodology originates with image pre-processing, comprising scaling, normalization, and augmentation, to strengthen model resilience. Subsequently, the feature extraction is accomplished inside the CNN architecture layers. Moreover, the use of transfer learning harnesses the proposed CNN model, fine-tuned for the particular job of the Eagle-Eye vision engine for bird species classification. The proposed pre-trained model can also be used for other classification tasks. This technique attains a remarkable accuracy of 89% due to its large dataset and holds potential for further applications across avian taxonomy. In summary, CNNs offer an effective avenue for automated bird or avian classification, permitting more efficient and exact bird behavior study thus enhancing avian conservation attempts.