Herbs identification trough leaves recognition / Hayatun Syamila Sholeh

The leaf contains important information for plant identification including herb plants. This project presents a prototype designed to recognize herbs plant using leaf images. The prototype focuses on six types of herbs plants available in Malaysia, that are Cabang Tiga, Bebuas, Kaduk, Ginseng Jawa,...

Full description

Bibliographic Details
Main Author:	Sholeh, Hayatun Syamila
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Flowers and flower culture. Ornamental plants Applied optics. Photonics
Online Access:	http://ir.uitm.edu.my/id/eprint/14522/ http://ir.uitm.edu.my/id/eprint/14522/1/TD_HAYATUN%20SYAMILA%20SHOLEH%20CS%2015_5.pdf

id	uitm-14522
recordtype	eprints
spelling	uitm-145222016-08-24T04:56:17Z http://ir.uitm.edu.my/id/eprint/14522/ Herbs identification trough leaves recognition / Hayatun Syamila Sholeh Sholeh, Hayatun Syamila Flowers and flower culture. Ornamental plants Applied optics. Photonics The leaf contains important information for plant identification including herb plants. This project presents a prototype designed to recognize herbs plant using leaf images. The prototype focuses on six types of herbs plants available in Malaysia, that are Cabang Tiga, Bebuas, Kaduk, Ginseng Jawa, Sireh and Pegaga. The prototype uses Zernike Moment Invariant features and similarity match Template Matching Classification to classify the leaf. Twenty four images have been collected. Twelve from top and twelve from bottom view. All the images captured with white background. Firstly, the image will be resized, converted into grayscale and enhanced to get local range of the image. Then, the value of Zernike moment invariant is computed for each leaf image. The plant identification is performed using template matching where the rules for each leaf is constructed based on the collection of values of the Zernike moment of invariant for eight leaves from each herb type. The output of this prototype is the name of herbs species that have been tested with test image. Accuracy of recognition calculated by count how many true species name displayed using Precision and recall rate. It is found that the Zernike moment invariant feature enables the template matching classifier to identify the herb better based on the bottom view of the leaves image. Thus, the finding from this prototype can provide useful information for developing an automated plant identification tool. 2015 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/14522/1/TD_HAYATUN%20SYAMILA%20SHOLEH%20CS%2015_5.pdf Sholeh, Hayatun Syamila (2015) Herbs identification trough leaves recognition / Hayatun Syamila Sholeh. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Flowers and flower culture. Ornamental plants Applied optics. Photonics
spellingShingle	Flowers and flower culture. Ornamental plants Applied optics. Photonics Sholeh, Hayatun Syamila Herbs identification trough leaves recognition / Hayatun Syamila Sholeh
description	The leaf contains important information for plant identification including herb plants. This project presents a prototype designed to recognize herbs plant using leaf images. The prototype focuses on six types of herbs plants available in Malaysia, that are Cabang Tiga, Bebuas, Kaduk, Ginseng Jawa, Sireh and Pegaga. The prototype uses Zernike Moment Invariant features and similarity match Template Matching Classification to classify the leaf. Twenty four images have been collected. Twelve from top and twelve from bottom view. All the images captured with white background. Firstly, the image will be resized, converted into grayscale and enhanced to get local range of the image. Then, the value of Zernike moment invariant is computed for each leaf image. The plant identification is performed using template matching where the rules for each leaf is constructed based on the collection of values of the Zernike moment of invariant for eight leaves from each herb type. The output of this prototype is the name of herbs species that have been tested with test image. Accuracy of recognition calculated by count how many true species name displayed using Precision and recall rate. It is found that the Zernike moment invariant feature enables the template matching classifier to identify the herb better based on the bottom view of the leaves image. Thus, the finding from this prototype can provide useful information for developing an automated plant identification tool.
format	Thesis
author	Sholeh, Hayatun Syamila
author_facet	Sholeh, Hayatun Syamila
author_sort	Sholeh, Hayatun Syamila
title	Herbs identification trough leaves recognition / Hayatun Syamila Sholeh
title_short	Herbs identification trough leaves recognition / Hayatun Syamila Sholeh
title_full	Herbs identification trough leaves recognition / Hayatun Syamila Sholeh
title_fullStr	Herbs identification trough leaves recognition / Hayatun Syamila Sholeh
title_full_unstemmed	Herbs identification trough leaves recognition / Hayatun Syamila Sholeh
title_sort	herbs identification trough leaves recognition / hayatun syamila sholeh
publishDate	2015
url	http://ir.uitm.edu.my/id/eprint/14522/ http://ir.uitm.edu.my/id/eprint/14522/1/TD_HAYATUN%20SYAMILA%20SHOLEH%20CS%2015_5.pdf
first_indexed	2023-09-18T22:51:49Z
last_indexed	2023-09-18T22:51:49Z
_version_	1777417594592034816

Herbs identification trough leaves recognition / Hayatun Syamila Sholeh

Similar Items