Fine-Tuned EfficientNetB0 Model for Accurate Bird Species Identification and Multi-Classification

Bird species identification is critical to ecosystem maintenance and biodiversity preservation. Birds serve a number of functions, such as encouraging the growth of coral reefs, helping agriculture, and affecting landscapes. In ecological research, there is a need to identify and classify bird species. With the advancement of machine learning, bird species multi-classification has become much more manageable. Many bird species classification systems have been created due to the advancement of technology; such systems are often utilized in mobile or online applications that display the output of multi-classification model. This report describes application of EfficientNetB0 and MobileNetV3 in identifying bird species. While MobileNetV3 was able to obtain an accuracy of 95.07%, EfficientNetB0 performed better with an accuracy of 97.22%. The bird species categorization system that was used has therefore yielded outstanding results. These findings open the door for practical applications in ecological research and wildlife monitoring by showing the effectiveness of using modern deep learning algorithms for accurate and trustworthy bird species identification.