DEEP LEARNING BASED BIRD BREED CLASSIFICATION SYSTEM

Bird breed classification is a critical task for biodiversity conservation, ecological studies, and ornithology. Traditional methods of bird identification rely heavily on expert knowledge and manual observation, which are time-consuming and prone to human error. In this paper, we propose a Deep Learning-Based Bird Breed Classification System that leverages advanced convolutional neural networks (CNNs) to accurately identify bird breeds from images. The system is designed to process images captured in various environments, handling challenges such as varying lighting conditions, backgrounds, and bird postures. Our proposed system comprises several stages: image pre-processing, feature extraction using a deep CNN, breeds classification, and post-processing to enhance classification accuracy. The CNN model is trained on a large, annotated dataset of bird images, enabling it to learn intricate patterns and distinguishing features of different bird breeds. We evaluate the system’s performance using a diverse test set and demonstrate its superiority over traditional classification methods in terms of accuracy and robustness.