Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib

Planting a plant is one of a method to control a current globe temperature. Plant features recognition has been performed by several researchers previously. Wu et al. [1] has performed the leaf recognition by using Probabilistic Neural Network (PNN) in order to classify the plants. As a result Wu et...

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Main Authors:	Ab Jabal, Mohamad Faizal, Hamid, Suhardi, Shuib, Salehuddin
Format:	Research Reports
Language:	English
Published:	Institute of Research Management & Innovation (IRMI) 2013
Subjects:	Plant anatomy Plant physiology
Online Access:	http://ir.uitm.edu.my/id/eprint/20147/ http://ir.uitm.edu.my/id/eprint/20147/1/LP_MOHAMAD%20FAIZAL%20AB%20JABAL%20IRMI%20K%2013_5.pdf

id	uitm-20147
recordtype	eprints
spelling	uitm-201472019-02-13T04:37:17Z http://ir.uitm.edu.my/id/eprint/20147/ Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib Ab Jabal, Mohamad Faizal Hamid, Suhardi Shuib, Salehuddin Plant anatomy Plant physiology Planting a plant is one of a method to control a current globe temperature. Plant features recognition has been performed by several researchers previously. Wu et al. [1] has performed the leaf recognition by using Probabilistic Neural Network (PNN) in order to classify the plants. As a result Wu et al. [1] was successful developed an efficient algorithm for the plant classification. 32 kinds of plants have been classified by using the algorithm. The basic leaf features considered by the algorithm had been defined by Wu et al. [1] involved diameter of the leaf, physiological length, physiological width, leaf area and leaf perimeter. Moreover, from the basic leaf features, Wu et al. [1] had defined several digital morphological features which are involved smooth factor, aspect ratio, form factor, rectangularity, narrow factor, vein features and perimeter ratio of diameter, physiological length and width. Final result produced by the algorithm is 92.312% of average accuracy and the classification for the leaf was based on the leaf-shape information. Institute of Research Management & Innovation (IRMI) 2013-05 Research Reports NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/20147/1/LP_MOHAMAD%20FAIZAL%20AB%20JABAL%20IRMI%20K%2013_5.pdf Ab Jabal, Mohamad Faizal and Hamid, Suhardi and Shuib, Salehuddin (2013) Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib. [Research Reports] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Plant anatomy Plant physiology
spellingShingle	Plant anatomy Plant physiology Ab Jabal, Mohamad Faizal Hamid, Suhardi Shuib, Salehuddin Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
description	Planting a plant is one of a method to control a current globe temperature. Plant features recognition has been performed by several researchers previously. Wu et al. [1] has performed the leaf recognition by using Probabilistic Neural Network (PNN) in order to classify the plants. As a result Wu et al. [1] was successful developed an efficient algorithm for the plant classification. 32 kinds of plants have been classified by using the algorithm. The basic leaf features considered by the algorithm had been defined by Wu et al. [1] involved diameter of the leaf, physiological length, physiological width, leaf area and leaf perimeter. Moreover, from the basic leaf features, Wu et al. [1] had defined several digital morphological features which are involved smooth factor, aspect ratio, form factor, rectangularity, narrow factor, vein features and perimeter ratio of diameter, physiological length and width. Final result produced by the algorithm is 92.312% of average accuracy and the classification for the leaf was based on the leaf-shape information.
format	Research Reports
author	Ab Jabal, Mohamad Faizal Hamid, Suhardi Shuib, Salehuddin
author_facet	Ab Jabal, Mohamad Faizal Hamid, Suhardi Shuib, Salehuddin
author_sort	Ab Jabal, Mohamad Faizal
title	Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
title_short	Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
title_full	Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
title_fullStr	Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
title_full_unstemmed	Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
title_sort	hevea leaf features extraction and recognition algorithm for hevea clones classification using image / mohamad faizal ab jabal, suhardi hamid, salehuddin shuib
publisher	Institute of Research Management & Innovation (IRMI)
publishDate	2013
url	http://ir.uitm.edu.my/id/eprint/20147/ http://ir.uitm.edu.my/id/eprint/20147/1/LP_MOHAMAD%20FAIZAL%20AB%20JABAL%20IRMI%20K%2013_5.pdf
first_indexed	2023-09-18T23:04:04Z
last_indexed	2023-09-18T23:04:04Z
_version_	1777418365129719808

Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib

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