Automated Bird Species Identification Through Machine Learning Techniques

Suhil Shoukath, Kambali and Ushashree, R. (2024) Automated Bird Species Identification Through Machine Learning Techniques. Journal of Data Science, 2024 (34). pp. 1-8. ISSN 2805-5160

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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.

Item Type:	Article
Uncontrolled Keywords:	Bird species classification, machine learning, deep learning, ResNet, HOG, SIFT, biodiversity
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software S Agriculture > SF Animal culture
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	05 Nov 2024 01:25
Last Modified:	05 Nov 2024 01:25
URI:	http://eprints.intimal.edu.my/id/eprint/2013

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