Abstract :

Accurate Bird Species Identification Is Essential For Biodiversity Monitoring, Ecological Research, And Wildlife Conservation. Traditional Methods Relying On Manual Observation Or Handcrafted Features Are Time-consuming, Labor-intensive, And Prone To Errors, Especially When Dealing With Visually Similar Species. This Study Proposes A Deep Learning-based Approach Using Convolutional Neural Networks (CNNs) To Automatically Extract And Classify Features From Bird Images. The Model Leverages Techniques Such As Data Augmentation And Transfer Learning To Enhance Accuracy And Robustness Across Diverse Datasets. Experimental Results Demonstrate That The Proposed System Achieves High Precision And Efficiency, Offering A Scalable Solution For Real-time Bird Species Identification. This Approach Not Only Reduces Human Effort But Also Supports Ecological Monitoring And Conservation Efforts Effectively. Bird Species Identification Is A Challenging Task Due To The High Visual Similarity Among Species And Variations In Lighting, Pose, And Background. Traditional Methods Based On Manual Observation Or Classical Image Processing Are Often Time-consuming And Prone To Errors. This Study Proposes A Deep Learning-based Approach For Accurate And Automated Bird Species Identification. Convolutional Neural Networks (CNNs) Are Employed To Extract Distinctive Features Keywords: Bird Species, Deep Learning, Convolutional Neural Networks (CNN) Classification, Wildlife Monitoring, Automated Identification, Biodiversity Conservation

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

136-140

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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