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Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation

The agricultural industry has been, for a long time, dependent upon human expertise to detect plant disease. However, human experts may take years of training and can be inconsistent, as well as prone to fatigue. Presented in this thesis is the work conducted on utilising electronic nose incorpo...

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Main Author: Marni Azira, Markom
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis 2010
Subjects:
Basal stem rot (BSR)
Electronic nose
Odour
Plant disease
Oil palm industry
Artificial neural networks (ANN)
Ganoderma boninense
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/9876
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spelling my.unimap-98762010-10-18T12:27:42Z Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation Marni Azira, Markom Basal stem rot (BSR) Electronic nose Odour Plant disease Oil palm industry Artificial neural networks (ANN) Ganoderma boninense The agricultural industry has been, for a long time, dependent upon human expertise to detect plant disease. However, human experts may take years of training and can be inconsistent, as well as prone to fatigue. Presented in this thesis is the work conducted on utilising electronic nose incorporating artificial intelligence to detect plant malaise, specifically, basal stem rot (BSR) disease that is caused by Ganoderma boninense, a type of fungi affecting oil palm plantations in South East Asia. A commercial electronic nose, Cyranose 320, was used as the front-end sensors with artificial neural networks trained using Levenberg-Marquardt algorithm employed for decision making. For the first stage, a study on Cyranose 320 embedded pattern recognitions and artificial neural networks (ANNs) was conducted using a few types of essences. This stage confirmed that the ANNs is better than the embedded pattern recognitions in terms of accuracy and hence should be used for the next experiments. The second stage involved the Ganoderma boninense fruiting bodies detection in laboratory and oil palm plantation. This stage proved that the fungi odour can be detected after being tested using a few types of odour parameter. The next stage is to discriminate the healthy and non-healthy oil palm trunk in the plantation. The conducted work indicates that the combination of the electronic nose and ANNs has the ability to discriminate the infected trunk. The findings of the work were also used to develop an in-house low cost electronic nose to support further fundamental study and implementations. As a conclusion, this work confirms that it is feasible to utilise the electronic nose and ANNs to detect and discriminate the BSR disease both in the laboratory and in the plantation. 2010-10-18T12:27:42Z 2010-10-18T12:27:42Z 2009 Thesis http://hdl.handle.net/123456789/9876 en Universiti Malaysia Perlis School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Basal stem rot (BSR)
Electronic nose
Odour
Plant disease
Oil palm industry
Artificial neural networks (ANN)
Ganoderma boninense
spellingShingle Basal stem rot (BSR)
Electronic nose
Odour
Plant disease
Oil palm industry
Artificial neural networks (ANN)
Ganoderma boninense
Marni Azira, Markom
Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation
description The agricultural industry has been, for a long time, dependent upon human expertise to detect plant disease. However, human experts may take years of training and can be inconsistent, as well as prone to fatigue. Presented in this thesis is the work conducted on utilising electronic nose incorporating artificial intelligence to detect plant malaise, specifically, basal stem rot (BSR) disease that is caused by Ganoderma boninense, a type of fungi affecting oil palm plantations in South East Asia. A commercial electronic nose, Cyranose 320, was used as the front-end sensors with artificial neural networks trained using Levenberg-Marquardt algorithm employed for decision making. For the first stage, a study on Cyranose 320 embedded pattern recognitions and artificial neural networks (ANNs) was conducted using a few types of essences. This stage confirmed that the ANNs is better than the embedded pattern recognitions in terms of accuracy and hence should be used for the next experiments. The second stage involved the Ganoderma boninense fruiting bodies detection in laboratory and oil palm plantation. This stage proved that the fungi odour can be detected after being tested using a few types of odour parameter. The next stage is to discriminate the healthy and non-healthy oil palm trunk in the plantation. The conducted work indicates that the combination of the electronic nose and ANNs has the ability to discriminate the infected trunk. The findings of the work were also used to develop an in-house low cost electronic nose to support further fundamental study and implementations. As a conclusion, this work confirms that it is feasible to utilise the electronic nose and ANNs to detect and discriminate the BSR disease both in the laboratory and in the plantation.
format Thesis
author Marni Azira, Markom
author_facet Marni Azira, Markom
author_sort Marni Azira, Markom
title Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation
title_short Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation
title_full Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation
title_fullStr Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation
title_full_unstemmed Feasibility study of utilising electronic nose to detect BSR disease in oil palm plantation
title_sort feasibility study of utilising electronic nose to detect bsr disease in oil palm plantation
publisher Universiti Malaysia Perlis
publishDate 2010
url http://dspace.unimap.edu.my/xmlui/handle/123456789/9876
_version_ 1643789610360242176
score 13.222552

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