Automated Bird Species Identification Through Machine Learning Techniques
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...
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/2013/ http://eprints.intimal.edu.my/2013/1/jods2024_34.pdf http://eprints.intimal.edu.my/2013/2/553 |
| _version_ | 1848766897216028672 |
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| author | Suhil Shoukath, Kambali Ushashree, R. |
| author_facet | Suhil Shoukath, Kambali Ushashree, R. |
| author_sort | Suhil Shoukath, Kambali |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:58:26Z |
| format | Article |
| id | intimal-2013 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:26Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20132024-11-05T01:25:59Z http://eprints.intimal.edu.my/2013/ Automated Bird Species Identification Through Machine Learning Techniques Suhil Shoukath, Kambali Ushashree, R. QA75 Electronic computers. Computer science QA76 Computer software SF Animal culture 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. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2013/1/jods2024_34.pdf text en cc_by_4 http://eprints.intimal.edu.my/2013/2/553 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 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software SF Animal culture Suhil Shoukath, Kambali Ushashree, R. Automated Bird Species Identification Through Machine Learning Techniques |
| title | Automated Bird Species Identification Through Machine Learning
Techniques |
| title_full | Automated Bird Species Identification Through Machine Learning
Techniques |
| title_fullStr | Automated Bird Species Identification Through Machine Learning
Techniques |
| title_full_unstemmed | Automated Bird Species Identification Through Machine Learning
Techniques |
| title_short | Automated Bird Species Identification Through Machine Learning
Techniques |
| title_sort | automated bird species identification through machine learning
techniques |
| topic | QA75 Electronic computers. Computer science QA76 Computer software SF Animal culture |
| url | http://eprints.intimal.edu.my/2013/ http://eprints.intimal.edu.my/2013/ http://eprints.intimal.edu.my/2013/1/jods2024_34.pdf http://eprints.intimal.edu.my/2013/2/553 |