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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Bird Species Identification Using Deep Learning |
| Authors Name: | Akash Kumar , Abhishek Kumar , Arman Chaurasia , Gaurav Kumar Kashyap , Vishesh J |
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IJRTI_188892
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| Published Paper Id: | IJRTI2401028 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | This survey paper explores the imperative task of bird species identification, emphasizing the pivotal role of accurate avian classification in ecological preservation. Recognizing the inherent complexity of distinguishing diverse bird species, we propose a novel approach grounded in the integration of unsupervised learning within the domain of Deep Learning. Our methodology entails training a robust model on a diverse dataset that incorporates essential physical features, including color, wing patterns, and eye characteristics extracted from bird images. The unsupervised learning approach empowers the model to discern patterns without explicit labeling, allowing for a nuanced understanding of bird species based on their distinctive traits. Through rigorous evaluation like F1 score, recall, accuracy, and precision, our approach showcases promising results, demonstrating its efficacy in bird species identification. Beyond ecological studies, the uses of our model extend to wildlife monitoring, conservation endeavors, and citizen science initiatives, highlighting its more extensive effects on environmental awareness and stewardship. However, challenges such as data scarcity and environmental variability persist, necessitating ongoing research efforts. The paper concludes by discussing potential future directions, including the refinement of the model through additional feature incorporation, dataset expansion, and adaptation to diverse environmental conditions. By synthesizing advancements in Deep Learning with ornithological studies, this survey contributes to the evolving discourse at the intersection of technology and environmental conservation, paving the way for enhanced understanding and preservation of avian biodiversity. Birds play a vital part in maintaining ecological balance, making it essential to develop effective methods for bird species identification. This survey paper explores the application of unsupervised learning algorithms within the domain of Deep Learning to discuss the issue of identifying bird species. |
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| Cite Article: | "Bird Species Identification Using Deep Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.159 - 164, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401028.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2401028
Registration ID:188892
Published In: Volume 9 Issue 1, January-2024
DOI (Digital Object Identifier):
Page No: 159 - 164 Country: Bengaluru, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2401028 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2401028 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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