Automated approach for oil palm in vitro shoot classification

Oil palm tissue culture shows promising future in providing uniform and quality cloned ramets for planting. However, mass production of oil palm plantlet is currently prohibitive in spite of its high demand. This is due to the fact that most of the processes in tissue culture are done manually which...

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Main Author:	Ismail, Abdul Halim
Format:	Thesis
Language:	English
Published:	2010
Subjects:	Plant tissue culture - Automation Shoots (Botany) Oil palm - Classification
Online Access:	http://psasir.upm.edu.my/id/eprint/40728/1/FK%202010%2010R.pdf
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id	my-upm-ir.40728
record_format	uketd_dc
spelling	my-upm-ir.407282015-09-28T03:05:59Z Automated approach for oil palm in vitro shoot classification 2010-03 Ismail, Abdul Halim Oil palm tissue culture shows promising future in providing uniform and quality cloned ramets for planting. However, mass production of oil palm plantlet is currently prohibitive in spite of its high demand. This is due to the fact that most of the processes in tissue culture are done manually which is labour-intensive as well as prone to contamination. In order to mass-produce clonal planting materials, an automated system is desirable, which is expected to be more cost effective as well as enhancing efficiency. In this study, the automation system is targeted at the shoots development stage since it is tedious job and routine to be operated manually as no automation system is currently adaptable for the tasks. This research focuses on the classification of various categories of normal and abnormal oil palm in vitro shoots. The oil palm in vitro shoots samples were digitized and employed for automated approach. A customized method for automatic image thresholding has been proposed to deal with various samples geometrical orientations and a wide variety of lighting conditions, as the environment for the future automation system would likely to be this way. Features were later extracted based on thinning and convexity image morphologies. By manipulating the features data obtained, three classification methods have been experimented, namely Linear Discriminant Analysis, K-mean clustering and back-propagation neural network. Results showed that all classification methods perform well, and able to differentiate between normal and abnormal oil palm in vitro shoots, with highest classification rate at ninety three percent. This is expected to greatly facilitate the development of the prospective automation system. Plant tissue culture - Automation Shoots (Botany) Oil palm - Classification 2010-03 Thesis http://psasir.upm.edu.my/id/eprint/40728/ http://psasir.upm.edu.my/id/eprint/40728/1/FK%202010%2010R.pdf application/pdf en public masters Universiti Putra Malaysia Plant tissue culture - Automation Shoots (Botany) Oil palm - Classification
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Plant tissue culture - Automation Shoots (Botany) Oil palm - Classification
spellingShingle	Plant tissue culture - Automation Shoots (Botany) Oil palm - Classification Ismail, Abdul Halim Automated approach for oil palm in vitro shoot classification
description	Oil palm tissue culture shows promising future in providing uniform and quality cloned ramets for planting. However, mass production of oil palm plantlet is currently prohibitive in spite of its high demand. This is due to the fact that most of the processes in tissue culture are done manually which is labour-intensive as well as prone to contamination. In order to mass-produce clonal planting materials, an automated system is desirable, which is expected to be more cost effective as well as enhancing efficiency. In this study, the automation system is targeted at the shoots development stage since it is tedious job and routine to be operated manually as no automation system is currently adaptable for the tasks. This research focuses on the classification of various categories of normal and abnormal oil palm in vitro shoots. The oil palm in vitro shoots samples were digitized and employed for automated approach. A customized method for automatic image thresholding has been proposed to deal with various samples geometrical orientations and a wide variety of lighting conditions, as the environment for the future automation system would likely to be this way. Features were later extracted based on thinning and convexity image morphologies. By manipulating the features data obtained, three classification methods have been experimented, namely Linear Discriminant Analysis, K-mean clustering and back-propagation neural network. Results showed that all classification methods perform well, and able to differentiate between normal and abnormal oil palm in vitro shoots, with highest classification rate at ninety three percent. This is expected to greatly facilitate the development of the prospective automation system.
format	Thesis
qualification_level	Master's degree
author	Ismail, Abdul Halim
author_facet	Ismail, Abdul Halim
author_sort	Ismail, Abdul Halim
title	Automated approach for oil palm in vitro shoot classification
title_short	Automated approach for oil palm in vitro shoot classification
title_full	Automated approach for oil palm in vitro shoot classification
title_fullStr	Automated approach for oil palm in vitro shoot classification
title_full_unstemmed	Automated approach for oil palm in vitro shoot classification
title_sort	automated approach for oil palm in vitro shoot classification
granting_institution	Universiti Putra Malaysia
publishDate	2010
url	http://psasir.upm.edu.my/id/eprint/40728/1/FK%202010%2010R.pdf
_version_	1747811840808714240

Automated approach for oil palm in vitro shoot classification

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