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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主要作者: Sefidgar, Seyed Mohammad Hossein
格式: Thesis
语言:English
出版: 2014
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spelling my-upm-ir.600762024-11-25T01:28:15Z 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%2067.pdf text en public masters Universiti Putra Malaysia Neural networks (Computer science) Genetic algorithms Ahmad, Siti Anom
institution Universiti Putra Malaysia
collection PSAS Institutional Repository
language English
advisor Ahmad, Siti Anom
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%2067.pdf
_version_ 1818586123190927360