Plant leaf identification using moment invariants & general regression neural network

Living plant identification based on images of leaf is a very challenging task in the field of pattern recognition and computer vision. However, leaf classification is an important component of computerized living plant recognition. As inherent trait, leaf definitely contains important information f...

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Main Author:	Zulkifli, Zalikha
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11539/5/ZalikhaZulkifliMFSKSM2009TOC.pdf
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id	my-utm-ep.11539
record_format	uketd_dc
spelling	my-utm-ep.115392018-06-04T09:53:42Z Plant leaf identification using moment invariants & general regression neural network 2009-10 Zulkifli, Zalikha QA75 Electronic computers. Computer science Living plant identification based on images of leaf is a very challenging task in the field of pattern recognition and computer vision. However, leaf classification is an important component of computerized living plant recognition. As inherent trait, leaf definitely contains important information for plant species identification despite its complexity. The objective of this research is to identify the effectiveness of three moment invariant methods, namely Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI) and Tchebichef Moment Invariant (TMI) to extract features from plant leaf images. Then, the resulting set of features representing the leaf images are classified using General Regression Neural Network (GRNN) for recognition purposes. There are two main stages involved in plant leaf identification. The first stage is known as feature extraction process where moment invariant methods are applied. The output of this process is a set of a global vector feature that represents the shape of the leaf images. It is shown that TMI can extract vector feature with Percentage of Absolute Error (PAE) less than 10.38 percent. Therefore, TMI vector feature will be the input to second stage. The second stage involves classification of leaf images based on the derived feature gained in the previous stage. It is found that GRNN classifier produces 100 percent classification rate with average computational time of 0.47 seconds. 2009-10 Thesis http://eprints.utm.my/id/eprint/11539/ http://eprints.utm.my/id/eprint/11539/5/ZalikhaZulkifliMFSKSM2009TOC.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Zulkifli, Zalikha Plant leaf identification using moment invariants & general regression neural network
description	Living plant identification based on images of leaf is a very challenging task in the field of pattern recognition and computer vision. However, leaf classification is an important component of computerized living plant recognition. As inherent trait, leaf definitely contains important information for plant species identification despite its complexity. The objective of this research is to identify the effectiveness of three moment invariant methods, namely Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI) and Tchebichef Moment Invariant (TMI) to extract features from plant leaf images. Then, the resulting set of features representing the leaf images are classified using General Regression Neural Network (GRNN) for recognition purposes. There are two main stages involved in plant leaf identification. The first stage is known as feature extraction process where moment invariant methods are applied. The output of this process is a set of a global vector feature that represents the shape of the leaf images. It is shown that TMI can extract vector feature with Percentage of Absolute Error (PAE) less than 10.38 percent. Therefore, TMI vector feature will be the input to second stage. The second stage involves classification of leaf images based on the derived feature gained in the previous stage. It is found that GRNN classifier produces 100 percent classification rate with average computational time of 0.47 seconds.
format	Thesis
qualification_level	Master's degree
author	Zulkifli, Zalikha
author_facet	Zulkifli, Zalikha
author_sort	Zulkifli, Zalikha
title	Plant leaf identification using moment invariants & general regression neural network
title_short	Plant leaf identification using moment invariants & general regression neural network
title_full	Plant leaf identification using moment invariants & general regression neural network
title_fullStr	Plant leaf identification using moment invariants & general regression neural network
title_full_unstemmed	Plant leaf identification using moment invariants & general regression neural network
title_sort	plant leaf identification using moment invariants & general regression neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2009
url	http://eprints.utm.my/id/eprint/11539/5/ZalikhaZulkifliMFSKSM2009TOC.pdf
_version_	1747814869942403072

Plant leaf identification using moment invariants & general regression neural network

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