Year: 2024
Type: article
Abstract: The taxonomy of bird species is fundamental to ecological research, conservation efforts, and biodiversity monitoring. Traditional identification methods, which rely on field notes and visual assessments by trained ornithologists, are often labor-intensive, time-consuming, and prone to error. In recent years, machine learning algorithms and pre-trained models such as ResNet, Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT) have shown significant promise in automating bird species classification. This study explores the application of these advanced models in identifying bird species from visual data, discussing key challenges, methodologies, and the potential to achieve high classification accuracy with reliable confidence scores. By leveraging deep learning techniques, we aim to enhance the precision and scalability of bird taxonomy, supporting more efficient ecological studies and conservation practices.
Source: INTI JOURNAL.
Authors: Suhil Shoukath Kambali, Ushashree R.
Institution: Dayananda Sagar Academy of Technology and Management
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404084116