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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REFERENCES

[1] J.-X. Du, X.-F. Wang, and G.-J. Zhang, “Leaf shape based plant species recognition,”

Applied Mathematics and Computation, vol. 185, no. 2, pp. 883–893, Feb. 2007.

[2] L. Zhang, J. Kong, X. Zeng, and J. Ren, “Plant species identification based on neural

network,” in Proc. Fourth International Conference on Natural Computation, 2008, vol.

5, pp. 90–94.

[3] S. Wu, “Flavia, a leaf recognition algorithm for plant classification using Probabilistic

Neural Network (PNN),” 2007. [Online]. Available: http://flavia.sourceforge.net/.

[Accessed: 14-May-2012].

[4] N. Valliammal and S. N. Geethalakshmi, “An optimal feature subset selection for

leaf analysis,” International Journal of Computer and Communication Engineering, vol.

6, pp. 406–411, 2012.

[5] S. G. Wu, F. S. Bao, E. Y. Xu, Y. Wang, Y. Chang, and Q. Xiang, “A leaf recognition

algorithm for plant classification using probabilistic neural network,” in Proc. IEEE

International Symposium on Signal Processing and Information Technology, 2007, pp.

11–16.

[6] J. Tian, Q. Hu, X. Ma, and M. Han, “An improved KPCA/GA-SVM classification

model for plant leaf disease recognition,” Journal of Computational Information Systems,

vol. 8, no. 18, pp. 7737–7745, 2012.

[7] X. Li and Z. Chen, “Weed identification based on shape features and ant colony

optimization algorithm,” in Proc. International Conference on Computer Application and

System Modeling, 2010, vol. 1, pp. 39–50.

[8] J. Du, D. Huang, X. Wang, and X. Gu, “Shape recognition based on radial basis

probabilistic neural network and application to plant species identification,” in Proc.

Advances in Neural Networks, 2005, vol. 3497, no. 2, pp. 281–285.

[9] F. Ahmed, H. A. Al-Mamun, A. S. M. H. Bari, E. Hossain, and P. Kwan,

“Classification of crops and weeds from digital images: A support vector machine

approach,” Crop Protection., vol. 40, pp. 98–104, Oct. 2012.

[10] J. Liu, S. Zhang, and S. Deng, “A method of plant classification based on wavelet

transforms and support vector machines,” Emerging Intelligent Computing Technology

and Applications, pp. 253–260, 2009.

[11] S. Prasad, P. Kumar, and R. C. Tripathi, “Plant leaf species identification using

curvelet transform,” in Proc. 2nd International Conference on Computer and

Communication Technology, 2011, pp. 646–652.

[12] Z. Wang, B. Lu, Z. Chi, and D. Feng, “Leaf image classification with shape context

and SIFT descriptors,” in Proc. International Conference on Digital Image Computing

Techniques and Applications, 2011, pp. 650–654.

© COPYRIG

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54

[13] Y. Ye, C. Chen, C.T. Li, H. Fu, and Z. Chi, “A computerized plant species

recognition system,” in Proc. International Symposium on Intelligent Multimedia, Video,

and Speech Processing, 2004, pp. 723–726.

[14] L. Gao, X. Lin, M. Zhong, and J. Zeng, “A neural network classifier based on prior

evolution and iterative approximation used for leaf recognition,” in Proc. 2010 Sixth

International Conference on Natural Computation (ICNC), 2010, vol. 2 , pp. 1038–1043.

[15] J. C. Neto, G. E. Meyer, D. D. Jones, and A. K. Samal, “Plant species identification

using elliptic Fourier leaf shape analysis,” Computers and Electronics in Agriculture, vol.

50, no. 2, pp. 121–134, Feb. 2006.

[16] Z. Husin, A. Y. M. Shakaff, A. H. A. Aziz, R. S. M. Farook, M. N. Jaafar, U.

Hashim, and A. Harun, “Embedded portable device for herb leaves recognition using

image processing techniques and neural network algorithm,” Computers and Electronics

in Agricalture, vol. 89, pp. 18–29, Nov. 2012.

[17] M. Z. Rashad, B. S. El-Desouky, and M. S. Khawasik, “Plants images classification

based on textural features using combined classifier,” International Journal of Computer

Science & Information Technology, vol. 3, no. 4, pp. 93–100, 2011.

[18] M. Shabanzade, M. Zahedi, and S. A. Aghvami, “Combination of local descriptors

and global features for leaf recognition,” Signal and Image Processing: An International

Journal (SIPIJ). vol. 2, no. 3, pp. 23–31, 2011.

[19] A. Camargo and J. S. Smith, “Image pattern classification for the identification of

disease causing agents in plants,” Computers and Electronics in Agriculture, vol. 66, no.

2, pp. 121–125, May. 2009.

[20] S. T. Bow, Pattern recognition and image preprocessing, 2th ed. CRC Press, 2002,

p. 720.

[21] C. M. Bishop, Pattern recognition and machine learning, vol. 1. Springer New

York, 2006, p. 738.

[22] J. P. Marques De Sa, Pattern recognition: concepts, methods, and applications, 4th

ed. Springer New York, 2001, p. 318.

[23] M. Negnevitsky, Artificial intelligence: A guide to intelligent systems. Addison

Wesley/Pearson, 2011, p. 479.

[24] A. Koschan and M. Abidi, Digital color image processing. John Wiley & Sons,

2008, p. 450.

[25] M. Nixon, M. S. Nixon, and A. S. Aguado, Feature extraction & image processing

for computer vision. Academic Press, 2012, p. 609.

[26] B. Yegnanarayana, Artificial neural networks. PHI Learning Pvt. Ltd, 2009, p. 476.

© COPYRIG

HT UPM

55

[27] J. Heaton, Introduction to neural networks with Java. Heaton Research, 2008, p.

440.

[28] T. Kumar and K. Verma, “A Theory based on conversion of RGB image to gray

image,” International Journal of Computer Applications, vol. 7, no. 2, pp. 7–10, 2010.

[29] L. Rutkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada, Artificial

intelligence and soft computing, part I: The 10th International Conference on Artificial

Intelligence and Soft Computing , Zakopane, Poland, June13-17, 10th ed. Springer New

York, 2010, p. 710.

[30] C. R. Tosh and G. D. Ruxton, Modelling perception with artificial neural networks.

Cambridge University Press, 2010, p. 398.

[31] I. Mihajlović, Đ. Nikolić, N. Štrbac, and Ž. Živković, “Statistical modelling in

ecological management using the artificial neural networks (ANNs),” Serbian Journal of

Management, vol. 5, no. 1, pp. 39–50, 2010.

[32] M. Affenzeller, S. Wagner, S. Winkler, and A. Beham, Genetic algorithms and

genetic programming: modern concepts and practical applications. Taylor & Francis,

2009, p. 379.

[33] S. N. Sivanandam and S. N. Deepa, Introduction to genetic algorithms. Springer,

2007, p. 462.

[34] B. Cyganek, Object detection and recognition in digital images: theory and

practice. John Wiley & Sons, 2013, p. 552.

[35] G. Panchal, A. Ganatra, Y. P. Kosta, and D. Panchal, “Searching most efficient

neural network architecture using Akaike’s Information Criterion (AIC),” International

Journal of Computer Applications, vol. 1, no. 5, pp. 41–44, 2010.

[36] P. C. Naval Jr and J. P. T. Yusiong, “An evolutionary multi-objective neural network

optimizer with bias-based pruning heuristic,” in Proc. Advances in Neural Networks,

Springer, 2007, pp. 174–183.

[37] M. Gen, O. Katai, B. McKay, A. Namatame, R. A. Sarker, and B. T. Zhang,

Intelligent and evolutionary systems, 4th ed. Springer, 2010, p. 218.

[38] R. C. Gonzalez, Digital image processing using Matlab, 12th ed. McGraw-Hill

Education (India) Pvt Limited, 2010, p. 738.

[39] F. Albregtsen, “Statistical texture measures computed from gray level co-

occurrence matrices,” Image Process. Lab. Dep. Informatics, University. Oslo, pp. 1–14,

Jan. 2008.

[40] B. Shrestha, “Classification of plants using images of their leaves,” M.S. thesis,

Dept. Computer. Sci., Appalachian State Univ., Carolina, U.S., 2010.

© COPYRIG

HT UPM

56

[41] C. Arunpriya and A. S. Thanamani, “An Efficient leaf recognition algorithm for

plant classification using support vector machine,” in Proc. International Conference on

Pattern Recognition, Informatics and Medical Engineering, 2012, pp. 428–432.

[42] C. Pandolfi, S. Mugnai, E. Azzarello, S. Bergamasco, E. Masi, and S. Mancuso,

“Artificial neural networks as a tool for plant identification: a case study on Vietnamese

tea accessions,” Euphytica, vol. 166, no. 3, pp. 411–421, 2009.

[43] D. C. Montgomery and G. C. Runger, Applied statistics and probability for

engineers, 5th ed. John Wiley & Sons, 2010, p. 784.

[44] E. G. M. De Lacerda, “Model selection of RBF networks via genetic algorithms,”

Ph. D, Informatics Center of Pernambuco Federal University, Brazil March, 2003.

[45] J. B. Kadane and N. A. Lazar, “Methods and criteria for model selection,” Journal

of the American Statistical Association., vol. 99, no. 465, pp. 279–290, 2004.