Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network

Plant recognition system is a system that recognizes the species of plants automatically. The applications of this system are in medicine, botanical research and agriculture. In the recent years, lack of sufficient botanist increases the need for computerized system. Also, it can be seen that workin...

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Main Author:	Sefidgar, Seyed Mohammad Hossein
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Neural networks (Computer science) Genetic algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/60076/1/FK%202014%2067IR.pdf
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id	my-upm-ir.60076
record_format	uketd_dc
spelling	my-upm-ir.600762018-04-16T03:48:52Z Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network 2014-02 Sefidgar, Seyed Mohammad Hossein Plant recognition system is a system that recognizes the species of plants automatically. The applications of this system are in medicine, botanical research and agriculture. In the recent years, lack of sufficient botanist increases the need for computerized system. Also, it can be seen that working with these systems are more convenient and quick when dealing with huge data. The problem with the existing plant recognition system is the lack of method to find the best structure for their classifiers. This work presents some contributions to plant recognition system. Number of samples involving Flavia, Citrus and Coleus were collected. Then, suitable features including texture and shape were extracted from the dataset. Texture features involved the middle energy and the middle entropy and shape features involved statistical characterizations including variance, median, standard deviation and mean. Next, the classification was carried out. First, the best set of structures for feed forward neural network were found by multi objective parallel genetic algorithm. This approach regarded three criteria involving mean square error, Akaike information criterion and minimum description length to rate different feed forward neural network structures and to select the best set of them. Lastly, feed forward neural network with the best structures were applied to classify the dataset. This method resulted around 99% of classification rate. To conclude, multi objective parallel genetic algorithm can automatically tune feed forward neural network to classify the dataset with a good classification rate. Neural networks (Computer science) Genetic algorithms 2014-02 Thesis http://psasir.upm.edu.my/id/eprint/60076/ http://psasir.upm.edu.my/id/eprint/60076/1/FK%202014%2067IR.pdf text en public masters Universiti Putra Malaysia Neural networks (Computer science) Genetic algorithms
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Neural networks (Computer science) Genetic algorithms
spellingShingle	Neural networks (Computer science) Genetic algorithms Sefidgar, Seyed Mohammad Hossein Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
description	Plant recognition system is a system that recognizes the species of plants automatically. The applications of this system are in medicine, botanical research and agriculture. In the recent years, lack of sufficient botanist increases the need for computerized system. Also, it can be seen that working with these systems are more convenient and quick when dealing with huge data. The problem with the existing plant recognition system is the lack of method to find the best structure for their classifiers. This work presents some contributions to plant recognition system. Number of samples involving Flavia, Citrus and Coleus were collected. Then, suitable features including texture and shape were extracted from the dataset. Texture features involved the middle energy and the middle entropy and shape features involved statistical characterizations including variance, median, standard deviation and mean. Next, the classification was carried out. First, the best set of structures for feed forward neural network were found by multi objective parallel genetic algorithm. This approach regarded three criteria involving mean square error, Akaike information criterion and minimum description length to rate different feed forward neural network structures and to select the best set of them. Lastly, feed forward neural network with the best structures were applied to classify the dataset. This method resulted around 99% of classification rate. To conclude, multi objective parallel genetic algorithm can automatically tune feed forward neural network to classify the dataset with a good classification rate.
format	Thesis
qualification_level	Master's degree
author	Sefidgar, Seyed Mohammad Hossein
author_facet	Sefidgar, Seyed Mohammad Hossein
author_sort	Sefidgar, Seyed Mohammad Hossein
title	Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
title_short	Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
title_full	Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
title_fullStr	Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
title_full_unstemmed	Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
title_sort	automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
granting_institution	Universiti Putra Malaysia
publishDate	2014
url	http://psasir.upm.edu.my/id/eprint/60076/1/FK%202014%2067IR.pdf
_version_	1747812257654374400

Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network

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