A Pre-trained EfficientNetV2 Deep Learning Model for Birds Species Classification

Automated bird species classification plays a crucial role in ornithology, wildlife conservation, and ecological research. In this study, we present the application of the state-of-the-art EfficientNet V2 model for bird species classification, leveraging its superior performance and computational efficiency. Using a diverse dataset of bird images collected from various sources, we conducted a systematic evaluation of the model’s performance across multiple epochs. The training process demonstrated notable improvements in training accuracy, with the model achieving a maximum accuracy of 98.09% after fifteen epochs. Similarly, the test accuracy exhibited consistent growth, reaching 95.89% by the fifteenth epoch. Furthermore, the training and test loss values exhibited a consistent decrease, indicating the model’s robustness and generalization capability. These results underscore the effectiveness of the EfficientNet V2 model in accurately identifying bird species from images, highlighting its potential applications in ornithological research and biodiversity conservation efforts. Moving forward, further refinements and optimizations could enhance the model’s performance and broaden its utility in automated bird species identification tasks.