Image based Bird Species Identification using CNN

Recent advancements in computer vision and deep learning have significantly enhanced the potential for image-based bird species identification, garnering considerable interest in ornithology and ecological research. The proposed system investigates the application of Convolutional Neural Networks (CNNs) for the automated and precise identification of bird species from photographic images. A variety of bird picture datasets are gathered, and tagged with relevant species labels, and a CNN model for species classification is trained using this process. Utilizing its capacity to learn spatial hierarchies and hierarchical characteristics, CNN can capture complex patterns in the morphology of birds. Transfer learning techniques may enhance model performance, especially when dealing with limited labelled data. The study aims to address challenges such as fine-grained discrimination, data imbalance, and variability in environmental conditions. The relevance is in offering a dependable and expandable method for identifying bird species, supporting citizen science projects, biodiversity protection, and ecological monitoring. The suggested CNN-based technique is effective in reliably classifying various bird species from photos, as demonstrated by implementation results and model evaluations. This will facilitate the development of automated systems for identifying species names, characters, and other information. The implementation is carried out with several bird image datasets collected from “CUB-200-2011” and “Birdsnap” datasets. The Kaggle dataset was also used for this study, and output was gained using the Python tool.