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UNIVERSITI PUTRA MALAYSIA
AUTOMATED PLANT RECOGNITION SYSTEM BASED ON MULTI-OBJECTIVE PARALLEL GENETIC ALGORITHM AND
NEURAL NETWORK
SEYED MOHAMMAD HOSSEIN SEFIDGAR
FK 2014 67
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AUTOMATED PLANT RECOGNITION SYSTEM BASED ON MULTI-
OBJECTIVE PARALLEL GENETIC ALGORITHM AND NEURAL
NETWORK
By
SEYED MOHAMMAD HOSSEIN SEFIDGAR
Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,
in Fulfilment of the Requirements for the Degree of Master of Science
February 2014
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All material contained within the thesis including without limitation text, logos, icons,
photographs and all other artwork, is copyright material of Universiti Putra Malaysia
unless otherwise stated. Use may be made of any material contained within the thesis for
non-commercial purposes from the copyright holder. Commercial use of material may
only be made with the express, prior, written permission of Universiti Putra Malaysia.
Copyright © Universiti Putra Malaysia
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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment
of the requirement for the degree of Master of Science
AUTOMATED PLANT RECOGNITION SYSTEM BASED ON MULTI-
OBJECTIVE PARALLEL GENETIC ALGORITHM AND NEURAL
NETWORK
By
SEYED MOHAMMAD HOSSEIN SEFIDGAR
February 2014
Chair: Siti Anom Ahmad, PhD
Faculty: Engineering
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.
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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai
memenuhi keperluan untuk ijazah Master Sains
SISTEM PENGECAMAN TUMBUHAN AUTOMATIK BERDASARKAN
ALGORITMA GENETIK SELARI PELBAGAI-OBJEKTIF DAN RANGKAIAN
NEURAL
Oleh
SEYED MOHAMMAD HOSSEIN SEFIDGAR
Februari 2014
Pengerusi: Siti Anom Ahmad, PhD
Fakulti: Kejuruteraan
Sistem pengecaman tumbuhan adalah satu sistem yang mengecam spesies tumbuhan
secara automatik. Aplikasi sistem ini adalah dalam bidang perubatan, penyelidikan botani
dan pertanian. Sejak kebelakangan ini, kekurangan ahli botani meningkatkan keperluan
sistem berkomputer. Selain itu, ia boleh dilihat bahawa bekerja dengan sistem ini adalah
lebih mudah dan cepat apabila berurusan dengan data yang besar. Masalah dengan sistem
pengiktirafan tumbuhan adalah kekurangan kaedah untuk mencari struktur yang terbaik
untuk penjodoh bilangan mereka. Kerja ini membentangkan beberapa sumbangan untuk
menanam sistem pengiktirafan. Bilangan sampel yang melibatkan Flavia, Citrus dan
Coleus telah dikumpulkan. Kemudian, ciri-ciri yang sesuai termasuk tekstur dan bentuk
ini diperolehi daripada dataset itu. Ciri-ciri tekstur yang terlibat tenaga pertengahan dan
entropi pertengahan dan bentuk ciri-ciri yang terlibat pencirian statistik termasuk varians,
sisihan piawai dan min. Seterusnya, pengelasan yang telah dilakukan. Pertama, set terbaik
struktur untuk makanan rangkaian neural ke hadapan ditemui oleh objektif algoritma
selari genetik berbilang. Pendekatan ini dianggap tiga kriteria yang melibatkan ralat min
persegi, maklumat kriteria Akaike dan panjang deskriptif minimum untuk mengadar
makanan ke hadapan struktur rangkaian neural yang berbeza dan untuk memilih set yang
terbaik daripada mereka. Akhir sekali, makanan rangkaian neural ke hadapan dengan
struktur yang terbaik telah digunakan untuk mengelaskan dataset itu. Kaedah ini
menyebabkan sekitar 99 % daripada kadar pengelasan. Untuk menyimpulkan, objektif
algoritma selari genetik berbilang boleh secara automatik lagu makanan rangkaian neural
ke hadapan untuk mengklasifikasikan dataset dengan kadar pengelasan yang baik.
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ACKNOWLEDGEMENTS
To begin with, I would like thank my supervisor Dr. Siti Anom Ahmad for all her support
throughout my project.
Words fail me to express my appreciation to my parents for their inseparable support,
prayer and my beloved sister Sara.
My special thanks goes to all my friends. Finally, I would like to thank all my colleagues
for their valuable assistance in my project.
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I certify that a Thesis Examination Committee has met on February 2014 to conduct the
final examination of Seyed Mohammad Hossein Sefidgar on his thesis entitled
“AUTOMATED PLANT RECOGNITION SYSTEM BASED ON MULTI OBJECTIVE
PARALLEL GENETIC ALGORITHM AND NEURAL NETWORK” in accordance
with the Universities and University Colleges Act 1971 and the Constitution of the
Universiti Putra Malaysia [P.U. (A) 106] 15 March 1998. The Committee recommends
that the student be awarded the Master.
Members of the Thesis Examination Committee were as follows:
Chairperson, PhD
Prof. Dr. Hashim bin Hizam
Faculty of Engineering
Universiti Putra Malaysia
(Chairperson)
Name of Examiner 1, PhD
Assoc. Prof. Dr. Samsul Bahari Mohd Noor
Faculty of Engineering
Universiti Putra Malaysia
Name of Examiner 2, PhD
Assoc. Prof. Dr. Mohammad Hamiruce Marhaban
Faculty of Engineering
Universiti Putra Malaysia
Name of External Examiner, PhD
Assoc. Prof. Dr. Siti Norul Huda Sheikh Abdullah
Faculty Information Science & Technology
Universiti Kebangsaan Malaysia
Malaysia
______________________________
SEOW HENG FONG, PhD
Professor and Deputy Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been
accepted as fulfilment of the requirement for the degree of Master of Science.
The members of the Supervisory Committee were as follows:
Siti Anom Binti Ahmad, PhD
Senior Lecturer
Faculty of Engineering
Universiti Putra Malaysia
(Chairperson)
Raja Mohd Kamil Bin Raja Ahmad, PhD
Senior Lecturer
Faculty of Engineering
Universiti Putra Malaysia
(Member)
___________________________________
BUJANG BIN KIM HUAT, PhD
Professor and Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
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DECLARATION
Declaration by the student
I hereby confirm that:
this thesis is my original work
quotations, illustrations and citations have been duly referenced
the thesis has not been submitted previously or comcurrently for any other degree
at any institutions
intellectual property from the thesis and copyright of thesis are fully-owned by
Universiti Putra Malaysia, as according to the Universiti Putra Malaysia
(Research) Rules 2012;
written permission must be owned from supervisor and deputy vice –chancellor
(Research and innovation) before thesis is published (in the form of written,
printed or in electronic form) including books, journals, modules, proceedings,
popular writings, seminar papers, manuscripts, posters, reports, lecture notes,
learning modules or any other materials as stated in the Universiti Putra Malaysia
(Research) Rules 2012;
there is no plagiarism or data falsification/fabrication in the thesis, and scholarly
integrity is upheld as according to the Universiti Putra Malaysia (Graduate
Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia
(Research) Rules 2012. The thesis has undergone plagiarism detection software
Signature: _______________________ Date: __________________
Name and Matric No.: _________________________________________
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Declaration by Members of Supervisory Committee
This is to confirm that:
The research conducted and the writing of this thesis was under our
supervision;
Supervision responsibilities as stated in the Universiti Putra Malaysia
(Graduate Studies) Rules 2003 (Revision 2012-2013) are adhered to.
Signature: Signature:
Name of
Chairman of
Supervisory
Committee: Siti Anom Ahmad
Name of
Member of
Supervisory
Committee:
Raja Mohd Kamil
Raja Ahmad
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TABLE OF CONTENTS
Page ABSTRACT I
ABSTRAK II ACKNOWLEDGEMENT III
APPROVAL IV
DECLARATION VI
LIST OF TABLES X
LIST OF FIGURES XI
LIST OF ABBREVIATIONS XII
CHAPTER
1 INTRODUCTION 1
1.1 Problem Statement 1
1.2 Objectives 2
1.3 Scopes of the Work 2
1.4 Thesis Layout 2
2 LITERATURE REVIEW 4
2.1 Plant Recognition System 4
2.2 Optimized Plant Recognition System 9
2.3 Image Classification System Description 11
2.3.1 Image Preprocessing 12
2.3.2 Feature Extraction 13
2.3.3 Classification 14
2.3.4 Optimization 15
2.4 Summary 21
3 METHODOLOGY 22
3.1 Data Collection 23
3.1.1 Control Data 23
3.1.2 Real Data 23
3.2 Image Processing and Feature Extraction 24
3.2.1 Image Preprocessing 25
3.2.2 Feature Extraction 26
3.3 Classification 32
3.4 Development of Automated Plant Recognition System Tool 36
4 RESULTS AND DISCUSSION 39
4.1 Preprocessing 39
4.1.1 Conversion to Gray Level Image 39
4.1.2 Extracting ROI of the Image 41
4.2 Feature Extraction 43
4.2.1 Texture Based Analysis 43
4.2.2 Shape Based Analysis 43
4.3 Classification 45
4.4 System Evaluation 49
4.5 Discussion 49
4.6 Summary 50
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5 CONCLUSION AND FUTURE WORK 51
5.1 Conclusion 51
5.2 Future work 51
REFERENCES 53
PUBLICATIONS 57
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LIST OF TABLES
TABLE Page
2.1 The summary of the PRS literature 9
2.2 The summary of the optimized PRS literature 11
2.3 Advantages and disadvantages of the GA 18
3.1 Size of the classes and subclasses 24
4.1 RGB conversion to gray level images of Flavia samples 39
4.2 RGB conversion to gray level image of Citrus and Coleus data 40
4.3 ROI extraction for Flavia data 41
4.4 ROI extraction of Citrus and Coleus data 42
4.5 Middle entropy and energy results of the whole data 43
4.6 Shape based features of the selected plant samples 44
4.7 Parameters used for the MOPGA 45
4.8 Different structures proposed by the MOPGA for Flavia image data 48
4.9 Different structures proposed by the MOPGA for Citrus and Coleus 48
4.10 Classification results with different structures of FFNN proposed by
MOPGA for Citrus, Coleus and Flavia data
49
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LIST OF FIGURES
FIGURE Page
2.1 Classification procedures 12
2.2 Supervised and unsupervised classification examples 15
2.3 Picture of the phenotype and genotype 16
2.4 Simple continuous GA procedures 17
2.5 The Pareto front answer is the line comprised by the optimal solutions 20
3.1 Whole work flowchart 22
3.2
From left to right, Chinese Cinnamon, Winter Sweet, Sweet
Osmanthus, Oleander, Canadian Poplar respectively
23
3.3 From left to right, Coleus Blumi, Coleus Kong Salmon, Coleus Black
Dragon, Coleus Dark Chocolate
23
3.4 From left to right, lemon, kumquat, orange, sour orange respectively 23
3.5 Structure of the proposed image processing and feature extraction 24
3.6 Conversion of the RGB image to the gray level image for the Canadian
Poplar
25
3.7 Extraction ROI for the Canadian Poplar sample 26
3.8 From left, Input image, intensity matrix and GLCM 27
3.9 From right to left, arbitrary image symmetric and normalized GLCM
matrix respectively
27
3.10 Periodic behavior of the entropy 28
3.11 Constant behavior of middle entropy in different scales 28
3.12 Conversion from gray level image to binary image 30
3.13 The boundary point’s address extracted from the binary image 30
3.14 From right to left, original binary image, eroded image and their
subtraction (boundary of the image)
30
3.15 Classification flowchart 33
3.16 Flowchart of finding the best set of FFNN parameters by MOPGA 33
3.17 MOPGA flowchart 34
3.18 Matlab programming of feature extraction 36
3.19 Installation of MCR on windows 37
3.20 Loading Flavia dataset into the computer 37
3.21 Classification results including FFNN structures and classification rates 38
4.1 Different distributions shape having different SDs (𝜎) and VARs (𝜎2) 44
4.2 Different distributions with different means and medians 44
4.3 MOPGA simulation results for Flavia image 46
4.4 MOPGA simulation results for Coleus scanned images 46
4.5 MOPGA simulation results for Citrus images scanned image 47
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LIST OF ABBREVIATIONS
ACOFSS – Ant Colony Optimization Feature Subset Selection
AICC – Akaike Information Criterion Corrected
ANN – Artificial Neural Network
CCD – Centroid Counter Distance
FFNN – Feed Forward Neural Network
GA – Genetic Algorithm
GAFNN – Genetic Algorithm and Feed Forward Neural Network
GLCM – Gray-Level Co-Occurrence Matrix
ICA – Independent Component Analysis
IDSC – Inner Distance Shape Context
KPCA – Kernel Principal Component Analysis
LVQ – Learning Vector Quantization
MAP – Maximum A Prior
MDL – Minimum Description Length
MMC – Move Median Center
MOO – Multi Objective Optimization
MOPGA – Multi Objective Parallel Genetic Algorithm
MSE – Mean Square Error
OLSA – Orthogonal Least Square Algorithm
PCA – Principle Component Analysis
PGA – Parallel Genetic Algorithm
PNN – Probabilistic Neural Network
PRS – Plant Recognition System
RBF – Radial Basis Function
RBNN – Radial Basis Neural Network
RFNN – Radial Function Neural Network
RLSA – Recursive Least Square Algorithms
ROI – Region of Interest
SIFT – Scale Invariant Features Transform
SMPD – Statistical Minimized Point Distance
SOM – Self-Organizing Map
SVM – Support Vector Machine
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CHAPTER 1
INTRODUCTION
Plant Recognition System (PRS) is designed to assist botanists and, even, non-experts in
identifying plants. Conventionally, botanists applied daunting techniques like vegetation
key to identify right plant species. In this method, they needed to consider many
characters such as shape, margin and venation in a plant leaf to correctly recognize a kind
of plant. This method shows futile when dealing with the flora of a country or, even, a
continent, which are mega-data [1]. Nowadays, intelligent systems are engaged to
facilitate plant identification. Non-stop performance, portability and fastness of these
systems caused replacement of them with the conventional methods [2]. Therefore, there
is a demand to develop automated PRS to add more convenience and accuracy to it [3].
PRS is an intelligent system that benefits from machine learning and image processing
techniques. Also, in some works, optimization methods have been used to improve the
performance of PRS [4, 5]. The most prominent merit of this technique is convenient and
fast implementation. Also, another benefit of this method is its automatic character. The
main contribution of the optimization to PRS has been in the selection of the suitable
features from feature vector to omit undesirable information [6, 7]. However, works (i.e.,
[8]), also, used this technique to optimize processes for training classifier. The focus of
this thesis is on the improvement of the classification rate and automatic tuning of
classifier by engagement of the optimization technique.
Several attempts have been made to improve the PSR. Broadly speaking, PRS involves
data collection, image processing and feature extraction and classification. Each part of
the PRS carries the same share of importance, meaning that selection of suitable or
unsuitable methods in each part can increase or decrease the system performance.
Therefore, the system performance can be improved by contribution in different parts.
Conventional PRS didn’t recruit optimization methods. SVM, neural network, linear
discriminate analysis and back propagation neural networks are strong classifiers used in
optimized PRS [2, 9, 10, 11]. In addition, classifiers like KNN, Probabilistic Neural
Network (PNN), MMC and hyper sphere classification method has been applied to
conventionally classification of plants [1, 5, 12]. However, some classifiers including
statistical analysis, feature matching and combined classifier techniques showed weak
performance [13, 14, 1, 15].
Also, the recruitment of the optimization method in PRS has been received increasing
attention, in the recent research. Most of these works focus on the optimization of the
feature vectors that are exorbitant. Methods such as Ant Colony Optimization (ACO) and
Kernel Principle Component Analysis (KPC), which were used with SVM, that are
applied to feature subset selection showed prominent classification rate [7, 6]. However,
the approaches such as OLSA, RLSA and GA were used to optimize classifiers training
[8, 4]. Overall, methods that used optimized PRS showed better performance compared
to the non-optimized PRS. Therefore, the optimization techniques should be a part of PRS
for improvement of classification rate.
1.1 Problem Statement
In the lack of botanists, computer aided systems are the best choice in plant identification.
It is frequently proved that even a botanist expert cannot perform his works without the
assistance of computers, especially when they are dealing with the huge dataset [13]. This
drawback brought an increasing need for automated plant identification systems. User-
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friendly, quick performance and reliability are the prominent benefits of this system.
Existing PRS suffer from the choice of the best model for the classifiers. Most of them
applied rules of thumb or trial and error method to find the classifier structures and
parameters. There is no evidence whether these techniques can really help with tuning
the classifier. This measure, also, can assist improving the classification rate. To sum up,
problems are listed below:
1. Limitations of the existing PRS, such as lack of automatic classifier tuning and
accuracy issue, bring the need for improving the current system.
2. Handling and processing huge plant’s data in a country or, even, a continent
necessitate developing fast and accurate PRS.
3. Portability and flexibility of this system allow everyone to have a nonstop expert,
which is not a case with botanist.
From this point of view, improving the classification rate has been regarded
considerably.
1.2 Objectives
Overall, the aim of this project is the development of the optimized PRS, through
investigation of optimization of the classifier structure. To obtain this achievement,
specific objectives are listed follow:
1. To design an automated PRS
2. To utilize Multi Objective Parallel Genetic Algorithm (MOPGA) to find the best set
of structures for the Feed Forward Neural Network (FFNN)
3. To improve the classification rate applying tuned FFNN
1.3 Scopes of the Work
In the past, botanists and non-experts used key vegetation to identify the plants. This
technique was very time-consuming and daunting because they needed to find the similar
plant images through a large handbook to identify a right specific plant. Due to increasing
automated and intelligent systems and the convenience they serve, people get more
interested in using PRS. The core of this system is artificial intelligence and pattern
recognition. In the PRS based on the plants’ leaves images, first, plants images were
collected using image acquisition devices such as scanners and cameras. Therefore,
adequate number of plants images including Flavia, Citrus and Coleus were collected.
Then, data were characterized by image processing and feature extraction techniques to
be prepared for the next part of classification. Shape and texture based were two features
applied for data characterization. In addition, machine learning techniques were applied
to model the processed data. Therefore, for the last step of PRS, FFNN, which was chosen
for machine learning technique, was tuned by MOPGA and then applied for classification
of extracted features.
1.4 Thesis Layout
The thesis structure represents the stages of the PRS development based on the
optimization method. The layout of the thesis is as below:
Chapter 2 reviews the recent growths in the PRS that employed machine learning and
focuses on the conventional and optimized methods. Different methods such as Fast
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Fourier Transform (FFT), geometrical wavelet for the feature extraction and Move
Median Center (MMC) and FFNN for the classification and Orthogonal Least Square
Algorithm (OLSA) and Genetic Algorithm (GA) optimization.
Chapter 3 provides the information about the research methodology. Initially, image data
collection and feature extractions are covered. The last section of this chapter gives some
insights into the evolutionary optimization techniques to tune FFNN and classification of
the obtained data.
Chapter 4 analyses the method and the achievement of the research. Data collection,
feature extraction, tuning the FFNN and classification results are shown. Then the results
of the research are presented and discussed.
Chapter 5 concludes from the research findings and highlights of the results explores of
the open problems. In the last part includes some additional suggestions for improvement
of the future research.
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