A Performance Evaluation of Lightweight Deep Learning Approaches for Bird Recognition

Reliable identification of bird species is a critical task for many applications, such as conservation biology, biodiversity assessments, and monitoring bird populations. However, identifying birds in the wild by visual observation can be time-consuming and prone to errors. There is a growing need for efficient and accurate bird recognition methods that can help researchers and conservationists identify bird species quickly and reliably. In this paper, we present a comparative analysis of the performance of state-of-the-art deep convolutional neural networks on a significantly sized bird dataset. Our goal is to develop a more accurate and efficient bird recognition method that can be deployed on edge computing devices. The results show that lightweight networks as EfficientNetB0 provide a great accuracy (more than 97%) and low time of response with a small demand for technological resources. Our findings could provide a reliable means of identifying bird species in the wild, which is essential for many conservation and management efforts.