Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi

For numerous people nowadays, determining the species of birds and classifying them is getting challenging. To reliably describe bird species without relying on human labour, research has been done in this area. To identify and categorise bird species using digital images of their forms, colours, an...

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Main Author:	Azmi, Adam Izzat
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95534/1/95534.pdf
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id	my-uitm-ir.95534
record_format	uketd_dc
spelling	my-uitm-ir.955342024-05-31T02:52:49Z Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi 2024 Azmi, Adam Izzat Neural networks (Computer science) For numerous people nowadays, determining the species of birds and classifying them is getting challenging. To reliably describe bird species without relying on human labour, research has been done in this area. To identify and categorise bird species using digital images of their forms, colours, and patterns is the goal of this research. As part of the approach used in this project, a dataset of bird photos was gathered, the data was processed, and a Convolutional Neural Network model was trained to accurately identify and categorise the species of birds. The results of this study show the value of employing Convolutional Neural Network to identify birds because they successfully categorise birds in a variety of contexts with high accuracy rates. The actual work done includes data collecting from the Kaggle dataset, Convolutional Neural Network implementation, training the model, and performance evaluation. The acquired results demonstrate the potential of CNNs-based bird species categorization systems in raising interest in learning and increasing the success rate of monitoring bird populations. By offering fresh perspectives and approaches to the classification of bird species, this research advances the subject and creates new opportunities for global improvements in the study of animals. Finally, it is envisaged that the classification of bird species based on an image system will aid in expanding our understanding of and research into bird species, particularly in Malaysia. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95534/ https://ir.uitm.edu.my/id/eprint/95534/1/95534.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Media Ahmad, Khairul Adilah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ahmad, Khairul Adilah
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Azmi, Adam Izzat Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi
description	For numerous people nowadays, determining the species of birds and classifying them is getting challenging. To reliably describe bird species without relying on human labour, research has been done in this area. To identify and categorise bird species using digital images of their forms, colours, and patterns is the goal of this research. As part of the approach used in this project, a dataset of bird photos was gathered, the data was processed, and a Convolutional Neural Network model was trained to accurately identify and categorise the species of birds. The results of this study show the value of employing Convolutional Neural Network to identify birds because they successfully categorise birds in a variety of contexts with high accuracy rates. The actual work done includes data collecting from the Kaggle dataset, Convolutional Neural Network implementation, training the model, and performance evaluation. The acquired results demonstrate the potential of CNNs-based bird species categorization systems in raising interest in learning and increasing the success rate of monitoring bird populations. By offering fresh perspectives and approaches to the classification of bird species, this research advances the subject and creates new opportunities for global improvements in the study of animals. Finally, it is envisaged that the classification of bird species based on an image system will aid in expanding our understanding of and research into bird species, particularly in Malaysia.
format	Thesis
qualification_level	Bachelor degree
author	Azmi, Adam Izzat
author_facet	Azmi, Adam Izzat
author_sort	Azmi, Adam Izzat
title	Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi
title_short	Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi
title_full	Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi
title_fullStr	Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi
title_full_unstemmed	Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi
title_sort	bird species classification based on image using convolutional neural network / adam izzat azmi
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Media
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95534/1/95534.pdf
_version_	1804889963177705472

Bird species classification based on image using Convolutional Neural Network / Adam Izzat Azmi

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