Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari

The results of a study on identifying therapeutic herbs with convolutional neural networks (CNNs) model are presented in this publication. The goal of the project is to create a mobile app that could recognize several kinds of therapeutic herbs from photos. The proposed system was developed in 3 pha...

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Main Author:	Shaari, Muhammad Syuhaiman
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96338/1/96338.pdf
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id	my-uitm-ir.96338
record_format	uketd_dc
spelling	my-uitm-ir.963382024-06-04T07:05:13Z Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari 2024 Shaari, Muhammad Syuhaiman Neural networks (Computer science) The results of a study on identifying therapeutic herbs with convolutional neural networks (CNNs) model are presented in this publication. The goal of the project is to create a mobile app that could recognize several kinds of therapeutic herbs from photos. The proposed system was developed in 3 phases which are the preliminary phase, the design and implementation phase, and the evaluation phase. The study utilized a dataset with 5 classes of medicinal herbs that contain 100 to 122 range of leaf images. The pre-trained model, VGG16 is used as the base model because this project applies transfer learning. The app is designed to be an Android mobile app by using Android Studio and Visual Studio code. The model is evaluated by using 3 ratios of train-test split performance which are 80:20, 70:30, and 60:40. With just 4 incorrectly categorized photos, the 70:30 ratio produces the lowest misclassification rate. Performance graphs for 70:30 and 80:20 ratios indicate consistent declines in training and validation loss, with 70:30 closely trailing training loss. All ratios see a steady rise in training accuracy, with 70:30 yielding the highest. Validation accuracy reaches its maximum at 80:20, while 70:30 is not far behind. All things considered, the 70:30 ratio is the best option for model finalization since it achieves a balance between precision, applicability, and low misclassification. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96338/ https://ir.uitm.edu.my/id/eprint/96338/1/96338.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Canda, Roger
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Canda, Roger
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Shaari, Muhammad Syuhaiman Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari
description	The results of a study on identifying therapeutic herbs with convolutional neural networks (CNNs) model are presented in this publication. The goal of the project is to create a mobile app that could recognize several kinds of therapeutic herbs from photos. The proposed system was developed in 3 phases which are the preliminary phase, the design and implementation phase, and the evaluation phase. The study utilized a dataset with 5 classes of medicinal herbs that contain 100 to 122 range of leaf images. The pre-trained model, VGG16 is used as the base model because this project applies transfer learning. The app is designed to be an Android mobile app by using Android Studio and Visual Studio code. The model is evaluated by using 3 ratios of train-test split performance which are 80:20, 70:30, and 60:40. With just 4 incorrectly categorized photos, the 70:30 ratio produces the lowest misclassification rate. Performance graphs for 70:30 and 80:20 ratios indicate consistent declines in training and validation loss, with 70:30 closely trailing training loss. All ratios see a steady rise in training accuracy, with 70:30 yielding the highest. Validation accuracy reaches its maximum at 80:20, while 70:30 is not far behind. All things considered, the 70:30 ratio is the best option for model finalization since it achieves a balance between precision, applicability, and low misclassification.
format	Thesis
qualification_level	Bachelor degree
author	Shaari, Muhammad Syuhaiman
author_facet	Shaari, Muhammad Syuhaiman
author_sort	Shaari, Muhammad Syuhaiman
title	Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari
title_short	Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari
title_full	Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari
title_fullStr	Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari
title_full_unstemmed	Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari
title_sort	medicinal herb recognition using convolutional neural network (cnn) / muhammad syuhaiman shaari
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96338/1/96338.pdf
_version_	1804889986502230016

Medicinal herb recognition using convolutional neural network (CNN) / Muhammad Syuhaiman Shaari

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