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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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2013/1/jods2024_34.pdf http://eprints.intimal.edu.my/2013/2/553 http://eprints.intimal.edu.my/2013/ http://ipublishing.intimal.edu.my/jods.html |
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| Summary: | 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. |
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