INTELLIGENT NON-DESTRUCTIVE CLASSIFICATION OF JOSAPINE PINEAPPLE MATURITY USING ARTIFICIAL NEURAL NETWORK NAZRIYAH BINTI HAJI CHE ZAN @ CHE ZAIN MASTER OF ENGINEERING (ELECTRONICS) UNIVERSITI MALAYSIA PAHANG
UNIVERSITI MALAYSIA PAHANG DECLARATION OF THESIS AND COPYRIGHT Author s Full Name : Date of Birth : 15 March 1982 Nazriyah Binti Hj Che Zan @ Che Zain Title : Intelligent Non-Destructive Classification of Josapine Pineapple Maturity Using Artificial Neural Network Academic Session : Semester II 2015/2016 I declare that this thesis is classified as: CONFIDENTIAL RESTRICTED OPEN ACCESS (Contains confidential information under the Official Secret Act 1972) (Contains restricted information as specified by the organization where research was done) I agree that my thesis to be published as online open access (Full text) I acknowledge that Universiti Malaysia Pahang reserve the right as follows: 1. The Thesis is the Property of Universiti Malaysia Pahang. 2. The Library of Universiti Malaysia Pahang has the right to make copies for the purpose of research only. 3. The Library has the right to make copies of the thesis for academic exchange. Certified By: (Student s Signature) (Supervisor s Signature) ASSOC. PROF. DR. 820315-02-5936 KAMARUL HAWARI New IC / Passport Number Name of Supervisor Date : 26 September 2016 Date : 26 September 2016
SUPERVISORS DECLARATION We hereby declare that we have checked this thesis and in our opinion, this thesis is adequate in terms of scope and quality for the award of the degree of Master of Engineering (Electronics) Signature : Name of Supervisor : Dr Kamarul Hawari Bin Ghazali Position : Associate Professor Date : 26 September 2016
STUDENT S DECLARATION I hereby declare that the work in this thesis is my own except for quotations and summaries which have been duly acknowledged. The thesis has not been accepted for any degree and is not concurrently submitted for award of other degree. Signature : Name : Nazriyah Binti Hj Che Zan @ Che Zain ID Number : MEL 09006 Date : 26 September 2016
INTELLIGENT NON-DESTRUCTIVE CLASSIFICATION OF JOSAPINE PINEAPPLE MATURITY USING ARTIFICIAL NEURAL NETWORK NAZRIYAH BINTI HAJI CHE ZAN @ CHE ZAIN Thesis submitted in fulfillment of the requirements for the award of the degree of Master of Engineering (Electronics) Faculty of Electrical & Electronics Engineering UNIVERSITI MALAYSIA PAHANG SEPTEMBER 2016
TABLE OF CONTENTS DECLARATION TITLE PAGE DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS Page ii iii iv v vi ix xi xiv CHAPTER 1 INTRODUCTION 1.1 Background 1 1.2 Problem Statement 3 1.3 Motivation 4 1.4 Research Objectives 5 1.5 Scope of Study 5 1.6 Thesis Contributions 6 1.7 Thesis Outline 7 CHAPTER 2 LITERATURE REVIEW 2.1 Introduction 8 2.2 Pineapple Industry in Malaysia 8 2.3 Quality Descriptions of Pineapple 12 2.4 Computer Vision Potential 14 2.4.1 Computer Vision for Agricultural Products 14 2.4.2 Image Processing and Image Analysis 17 2.5 Image Segmentation 18 2.5.1 Thresholding-based Segmentation 20 2.5.2 Region-based Segmentation 23 vi
2.5.3 Edge-based Segmentation 24 2.6 Feature Extraction 25 2.6.1 Features Extraction Techniques 25 2.6.2 Color Features Extraction Techniques 26 2.6.3 Color Features Extraction Implementation in Agricultural 30 2.7 Classification Techniques 32 2.7.1 Intelligent Classification Systems 32 2.7.2 Pattern Recognition 35 2.8 Summary 39 CHAPTER 3 RESEARCH METHODOLOGY 3.1 Overview 41 3.2 Data Acquisition 43 3.2.1 Pineapple Samples 43 3.2.2 Image Acquisition 45 3.3 Image Segmentation 47 3.3.1 Discrimination of RGB Color Domain 48 3.3.2 Bracts Removal Using Noise Filtering Technique 50 3.3.3 Hotelling Transform Angle Projection and Centroid Calculation 55 3.3.4 Pineapple Crown Removal Using Minimum Symmetrical Edge Distance 57 3.3.5 Misclassification Error Measurement 58 3.4 Feature Extraction Using Color Moment (cm) 62 3.5 Classification of Pineapple Maturity Index 65 3.5.1 Linear Classification Using Thresholding Value 65 3.5.2 Artificial Neural Network (ANN) Classification 69 3.6 Summary 74 CHAPTER 4 RESULTS AND DISCUSSION 4.1 Introduction 75 4.2 Misclassification Error Result 76 4.3 Features Extraction Result 81 vii
4.4 Performance of Classification Algorithms 87 4.4.1 Linear Classification Results 88 4.4.2 Artificial Neural Network Results 90 4.5 Summary 97 CHAPTER 5 CONCLUSION AND FURTHER RECOMMENDATION 5.1 Conclusion 98 5.2 Limitations 99 5.3 Recommendation for Further Reseaerch 100 REFERENCES 101 APPENDIX A 113 APPENDIX B 114 APPENDIX C 126 viii
LIST OF TABLES Table No. Title Page 2.1 Advantages and disadvantages of computer vision systems 16 2.2 Advantages and disadvantages of colour feature extraction techniques 30 2.3 Examples of pattern recognition applications 36 2.4 3.1 State of the art in Pineapple maturity sorting using computer vision Number of samples according to maturity index and usage purpose 40 44 3.2 Group pixel value of color component 67 3.3 New Maturity indices reference for percentage of yellowish 68 3.4 New maturity indices reference for R channel image 69 3.5 4.1 4.2 4.3 Pattern combination of features vector as input using in pattern recognition network algorithm Misclassification error of three (3) types of Structuring Element with R parameter on Red channel images Misclassification error of three (3) types of Structuring Element with R parameter on Green channel images Misclassification error of three (3) types of Structuring Element with R parameter on Blue channel images 71 77 78 79 4.4 Average misclassification error on R, G and B images 80 4.5 Accuracy of misclassification error on R, G and B images 80 4.6 4.7 4.8 4.9 Linear classification using threshold value from percentage of yellowish Linear classification using threshold value from average pixel values of color component from R channel. Accuracy of classification for linear thresholding using percentage of yellowish and average pixel values of color component from R channel Comparison of pineapples obtained using multiple N values 89 89 90 91 ix
4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4.18 4.19 for Pattern 1 Comparison of pineapples obtained using multiple N values for Pattern 2 Comparison of pineapples obtained using multiple N values for Pattern 3 Comparison of pineapples obtained using multiple N values for Pattern 4 Comparison of pineapples obtained using multiple N values for Pattern 5 Accuracy of classification for Pattern 1 during classification process Accuracy of classification for Pattern 2 during classification process Accuracy of classification for Pattern 3 during classification process Accuracy of classification for Pattern 4 during classification process Accuracy of classification for Pattern 5 during classification process Average of accuracy for every pattern combination during classification process 91 91 92 92 93 93 94 94 94 95 4.20 Comparison of classification result using Neural Network 95 4.21 4.22 4.23 4.24 Classification made by pattern recognition network for Pattern 2 with N=20 Classification made by pattern recognition network for Pattern 2 with N=40 Classification made by pattern recognition network for Pattern 2 with N=20 Classification made by Shuhairie for N36 pineapples using Back-Propagation Neural Network (BPNN) 96 97 97 98 x
LIST OF FIGURES Figure No. Title Page 1.1 Fresh pineapple post-harvesting operation 3 2.1 Top Pineapple Exporters In 1962 10 2.2 Malaysia pineapple production from 1970 until 2009 10 2.3 Malaysia pineapple export from 1970 until 2009 11 2.4 FAMA standard for pineapple maturity classification 13 2.5 Steps and levels in image processing 17 2.6 Image segmentation techniques 19 2.7 Intensity histograms that can be partitioned by a single threshold 21 2.8 Intensity histograms that can be partitioned by dual thresholds 22 2.9 Exemplary of RGB image and its corresponding histogram 27 2.10 Exemplary of Gray-scale image and its corresponding histogram 28 2.11 Multilayered perceptron network 33 2.12 Model for statistical pattern recognition 37 2.13 Illustration of a biological neuron 38 2.14 Illustration of an artificial neuron 39 3.1 General proposed methodology of pineapple classification using image processing technique 43 3.2 Pineapple sample of different maturity indexes and sizes 45 3.3 Image acquisition system 46 3.4 General proposed technique of Josapine pineapple image segmentation 47 3.5 Original RGB image 48 3.6 (a) Red channel, (b) Green channel, (c) Blue channel 48 xi
3.7 Binary image of every channel R, G and B 49 3.8 Fillhole image after noise filtering process 51 3.9 Shapes of structuring element (a) disk (b) diamond (c) octagon 53 3.10 Image after morphologically binary smoothing 54 3.11 Principle of Hotelling transform 55 3.12 Hotelling transform 56 3.13 (a) Farthest right and left pixels remove (b) Crown remove of pineapple binary image 57 3.14 Binary ground-truth image F O using Image J software 59 3.15 Performance evaluation of thresholding technique 60 3.16 Evaluating performance of thresholding algorithms on examplary image 3.17 (a) Binary mask in original angle (b) Segmented pineapple body using (a) as a mask 61 63 3.18 Threshold value determination from percentage of yellowish 67 3.19 Threshold value determination from R channel image 68 3.20 Block diagram of details used in ANN classification for Pattern 1 3.21 Block diagram of details used in ANN classification for Pattern 2 3.22 Block diagram of details used in ANN classification for Pattern 3 3.23 Block diagram of details used in ANN classification for Pattern 4 3.24 Block diagram of details used in ANN classification for Pattern 5 72 72 73 73 74 4.1 Maximum of intensity values inside ROI of R channel image 81 4.2 Maximum of intensity values inside ROI of G channel image 82 4.3 Maximum of intensity values inside ROI of B channel image 82 xii
4.4 Minimum of intensity values inside ROI of R channel image 83 4.5 Minimum of intensity values inside ROI of G channel image 83 4.6 Minimum of intensity values inside ROI of B channel image 84 4.7 Average of intensity values inside ROI of R channel image 84 4.8 Average of intensity values inside ROI of G channel image 85 4.9 Average of intensity values inside ROI of B channel image 85 4.10 Standard deviation of intensity values inside ROI of R channel image 4.11 Standard deviation of intensity values inside ROI of G channel image 4.12 Standard deviation of intensity values inside ROI of B channel image 86 86 87 xiii
LIST OF ABBREVIATIONS ANN CCV CM ECER EMM FAMA FAOSTAT GLCM LPNM MARDI MHD MLP MPIB ME NU RAE RBF ROI Artificial Neural Network Color Coherence Vector Color Moments East Coast Economic Region Edge Mismatch Federal Agriculture Marketing Agency (Malaysia) Food and Agriculture Organization of The United Nations Grey Level Co-occurrence Matrix Lembaga Perindustrian Nanas Malaysia Malaysia Agricultural Research and Development Institute Modified Hausdorff Distance Multi Layer Perceptron Malaysia Pineapple Industrial Board Misclassification Error Non-uniformity Relative Foreground Area Error Radial Basis Function Region of Interest xiv