Endangered Bird Species Classification using Machine Learning

Abstract

The conservation of endangered bird species is a critical aspect of biodiversity preservation. Traditional methods of identifying and classifying these species are often labor-intensive and time-consuming. In recent years, advancements in machine learning have offered promising alternatives for enhancing the accuracy and efficiency of such tasks. This paper explores the application of various machine learning algorithms to the classification of endangered bird species. By leveraging a dataset comprising images and audio recordings of bird calls, we train and evaluate models such as Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for audio classification. Our results demonstrate that these models can achieve high accuracy rates, significantly surpassing traditional methods. Furthermore, we discuss the importance of feature selection, data augmentation, and the integration of multimodal data in improving model performance. The findings underscore the potential of machine learning to revolutionize wildlife conservation efforts, providing a scalable and robust tool for the timely identification and protection of endangered bird species.