Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid

Rabbit breed classification poses challenges due to the diverse physical characteristics and colour patterns, complicating differentiation, especially for untrained individuals. Misclassification can detrimentally affect mating, genetic selection, and rabbit health. Thus, developing an accurate clas...

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Main Author:	Mohd Zaid, Aishah Nabila
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95549/1/95549.pdf
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id	my-uitm-ir.95549
record_format	uketd_dc
spelling	my-uitm-ir.955492024-05-31T02:52:43Z Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid 2024 Mohd Zaid, Aishah Nabila Neural networks (Computer science) Rabbit breed classification poses challenges due to the diverse physical characteristics and colour patterns, complicating differentiation, especially for untrained individuals. Misclassification can detrimentally affect mating, genetic selection, and rabbit health. Thus, developing an accurate classification system is crucial for the breeding and farming industry. This project aims to study the CNN algorithm's efficacy in rabbit breed classification, develop a CNN-based prototype, and evaluate its accuracy. Four widely raised rabbit breeds in Malaysia—Californian, Holland Lop, Lionhead, and New Zealand—are selected for classification due to their distinct characteristics. The research methodology encompasses preliminary study, design and implementation, and evaluation phases. Literature review, knowledge acquisition, and dataset collection inform algorithm selection and dataset validation. The design and implementation phase focus on prototype development, while the evaluation phase assesses classification performance. Results demonstrate the CNN algorithm's potential for achieving high accuracy, with an average accuracy of 95%. This study underscores the CNN algorithm's viability for accurate rabbit breed identification. It concludes by recommending further research into its application in other animal classification contexts, highlighting broader implications. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95549/ https://ir.uitm.edu.my/id/eprint/95549/1/95549.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Raju, Rajeswari
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Raju, Rajeswari
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mohd Zaid, Aishah Nabila Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid
description	Rabbit breed classification poses challenges due to the diverse physical characteristics and colour patterns, complicating differentiation, especially for untrained individuals. Misclassification can detrimentally affect mating, genetic selection, and rabbit health. Thus, developing an accurate classification system is crucial for the breeding and farming industry. This project aims to study the CNN algorithm's efficacy in rabbit breed classification, develop a CNN-based prototype, and evaluate its accuracy. Four widely raised rabbit breeds in Malaysia—Californian, Holland Lop, Lionhead, and New Zealand—are selected for classification due to their distinct characteristics. The research methodology encompasses preliminary study, design and implementation, and evaluation phases. Literature review, knowledge acquisition, and dataset collection inform algorithm selection and dataset validation. The design and implementation phase focus on prototype development, while the evaluation phase assesses classification performance. Results demonstrate the CNN algorithm's potential for achieving high accuracy, with an average accuracy of 95%. This study underscores the CNN algorithm's viability for accurate rabbit breed identification. It concludes by recommending further research into its application in other animal classification contexts, highlighting broader implications.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Zaid, Aishah Nabila
author_facet	Mohd Zaid, Aishah Nabila
author_sort	Mohd Zaid, Aishah Nabila
title	Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid
title_short	Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid
title_full	Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid
title_fullStr	Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid
title_full_unstemmed	Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid
title_sort	rabbit breed classification using cnn / aishah nabila mohd zaid
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95549/1/95549.pdf
_version_	1804889963931631616

Rabbit breed classification using CNN / Aishah Nabila Mohd Zaid

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