Feathers and Pixels: A Comparative Analysis of CNN Models for Efficient Bird Species Identification - George, Pretty Liju; Nedunchezhian, R. (Journal of Information Systems Research and Practice, 2024)
Bird species identification is crucial for biodiversity conservation, ecosystem monitoring, and avian research. It helps assess population trends, monitor ecosystem health, and track environmental changes. Birds are key indicators of habitat quality and climate impacts. Accurate identification supports studies on migration, breeding, and foraging, aiding conservation efforts and identifying threatened species. Deep learning technology, specifically Convolutional Neural Networks (CNNs), is used for bird species identification by training models on large labeled datasets of bird images. The CNN architecture is designed to extract features from images, and the model is trained to associate these features with bird species labels. The paper navigates through the nuances of model performance, facilitating a comprehensive understanding of their strengths and limitations. Metrics such as accuracy, recall, and precision are scrutinized, offering valuable insights for researchers and practitioners. The few models that were picked out for performance analysis are VGG16, VGG19, ResNet50, DenseNet, InceptionV3, and EfficientNet. The analysis was performed on 200 sample images, each of 50 different species, taken from Google’s dataset. This comparative study welcomes the reader to the intersection of avian ecology and deep learning, where feathers meet pixels to unlock new dimensions in bird species identification.