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Butterfly Species Recognition Using Convolutional Neural Network
Subject area: Science,Engineering and Technology · Area of research: Machine Learning, Deep learning
Abstract
In the realm of biodiversity research, accurately identifying butterfly species is a critical task, contributing to our understanding of ecosystems and supporting conservation efforts. This study introduces an innovative approach to this challenge through the development of a Butterfly Species Recognition System, harnessing the capabilities of Convolutional Neural Networks (CNNs). Butterflies, with their diverse and visually intricate patterns, pose a unique classification challenge, requiring a sophisticated model for effective species differentiation. The proposed CNN-based model is designed to automatically extract and analyze intricate features within butterfly images, facilitating a nuanced and accurate classification of different species. Through exposure to a diverse dataset encompassing various butterfly species, the model learns to recognize subtle patterns and variations, ensuring robustness and adaptability to real-world scenarios. The experimental results showcase the efficacy of the developed system, demonstrating high accuracy in butterfly species identification. This automated approach streamlines the identification process, holding promise for citizen science initiatives and large-scale biodiversity monitoring programs. By harnessing CNN capabilities, this research highlights the potential of cutting-edge technology to revolutionize butterfly species recognition, advancing our comprehension of ecological dynamics and aiding conservation endeavors.
Keywords
Butterfly species, Convolutional Neural Network, Image Recognition, Biodiversity, Classification.
How to cite this paper
@article{1705260,
author = {Amit Kumar Pandey, Dr. Santosh Singh, Ankush Sushil Singh, Ashwani Kumar Mishra, Bipin Yadav},
title = {Butterfly Species Recognition Using Convolutional Neural Network},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {6},
pages = {70-74},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17052601.pdf},
abstract = {In the realm of biodiversity research, accurately identifying butterfly species is a critical task, contributing to our understanding of ecosystems and supporting conservation efforts. This study introduces an innovative approach to this challenge through the development of a Butterfly Species Recognition System, harnessing the capabilities of Convolutional Neural Networks (CNNs). Butterflies, with their diverse and visually intricate patterns, pose a unique classification challenge, requiring a sophisticated model for effective species differentiation. The proposed CNN-based model is designed to automatically extract and analyze intricate features within butterfly images, facilitating a nuanced and accurate classification of different species. Through exposure to a diverse dataset encompassing various butterfly species, the model learns to recognize subtle patterns and variations, ensuring robustness and adaptability to real-world scenarios. The experimental results showcase the efficacy of the developed system, demonstrating high accuracy in butterfly species identification. This automated approach streamlines the identification process, holding promise for citizen science initiatives and large-scale biodiversity monitoring programs. By harnessing CNN capabilities, this research highlights the potential of cutting-edge technology to revolutionize butterfly species recognition, advancing our comprehension of ecological dynamics and aiding conservation endeavors.},
keywords = {Butterfly species, Convolutional Neural Network, Image Recognition, Biodiversity, Classification.},
month = {December},
}