Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza

The daisy family is one of the largest plant families in the world. There are numerous uses for various types of daisy species. The daisy species is the main subject of this study. Traditional method of classifying daisy species can be difficult. Most herbalists and traditional healers have trouble...

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Main Author:	Khaimuza, Haris Hidayatullah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95552/1/95552.pdf
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id	my-uitm-ir.95552
record_format	uketd_dc
spelling	my-uitm-ir.955522024-05-31T01:45:15Z Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza 2024 Khaimuza, Haris Hidayatullah Neural networks (Computer science) The daisy family is one of the largest plant families in the world. There are numerous uses for various types of daisy species. The daisy species is the main subject of this study. Traditional method of classifying daisy species can be difficult. Most herbalists and traditional healers have trouble identifying the proper species of daisy. Aside from that, the traditional classification method is costly and time-consuming. It is also important to consider the loss of traditional practices and the lack of cultural knowledge of plants. The first objective of this study is to study the Convolutional Neural Network (CNN) algorithm in the classification of daisy species based on image. Second objective is to develop the prototype of daisy species classification based on image using CNN algorithm. The last objective is to evaluate the accuracy of CNN model in the daisy species classification based on image. Daisy Species Classification Based on Image (DSC) will help to classify daisy species more quickly and accurately to solve all the issues mentioned. The result of this study is the classification model obtained 88% accuracy on the testing set. Several improvements can be made to this project which are expanding dataset using augmentation techniques, implementing multiple images classification, and expanding the model to classify more diverse species of daisy. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95552/ https://ir.uitm.edu.my/id/eprint/95552/1/95552.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Ahmad Baidowi, Zaid Mujaiyid Putra
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ahmad Baidowi, Zaid Mujaiyid Putra
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Khaimuza, Haris Hidayatullah Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
description	The daisy family is one of the largest plant families in the world. There are numerous uses for various types of daisy species. The daisy species is the main subject of this study. Traditional method of classifying daisy species can be difficult. Most herbalists and traditional healers have trouble identifying the proper species of daisy. Aside from that, the traditional classification method is costly and time-consuming. It is also important to consider the loss of traditional practices and the lack of cultural knowledge of plants. The first objective of this study is to study the Convolutional Neural Network (CNN) algorithm in the classification of daisy species based on image. Second objective is to develop the prototype of daisy species classification based on image using CNN algorithm. The last objective is to evaluate the accuracy of CNN model in the daisy species classification based on image. Daisy Species Classification Based on Image (DSC) will help to classify daisy species more quickly and accurately to solve all the issues mentioned. The result of this study is the classification model obtained 88% accuracy on the testing set. Several improvements can be made to this project which are expanding dataset using augmentation techniques, implementing multiple images classification, and expanding the model to classify more diverse species of daisy.
format	Thesis
qualification_level	Bachelor degree
author	Khaimuza, Haris Hidayatullah
author_facet	Khaimuza, Haris Hidayatullah
author_sort	Khaimuza, Haris Hidayatullah
title	Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
title_short	Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
title_full	Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
title_fullStr	Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
title_full_unstemmed	Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
title_sort	daisy species classification based on image using convolutional neural network algorithm / haris hidayatullah khaimuza
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95552/1/95552.pdf
_version_	1804889964681363456

Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza

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