CLASSIFICATION OF PLANTS USING IMAGES OF THEIR LEAVES A Thesis by BIVA SHRESTHA December 2010 APPROVED BY _____________________________________ Rahman Tashakkori Chairperson, Thesis Committee _____________________________________ Alice A. McRae Member, Thesis Committee _____________________________________ Zack E. Murrell Member, Thesis Committee _____________________________________ James T. Wilkes Chairperson, Computer Science _____________________________________ Edelma Huntley Dean, Research and Graduate Studies CLASSIFICATION OF PLANTS USING IMAGES OF THEIR LEAVES A Thesis by BIVA SHRESTHA December 2010 Submitted to the Graduate School Appalachian State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2010 Major Department: Computer Science Copyright by Biva Shrestha 2010 All Rights Reserved. ABSTRACT CLASSIFICATION OF PLANTS USING IMAGES OF THEIR LEAVES Biva Shrestha M.S., Appalachian State University Thesis Chairperson: Rahman Tashakkori Plant recognition is a matter of interest for scientists as well as laymen. Computer aided technologies can make the process of plant recognition much easier; botanists use morphological features of plants to recognize them. These features can also be used as a basis for an automated classification tool. For example, images of leaves of different plants can be studied to determine effective algorithms that could be used in classifying different plants. In this thesis, those salient features of plant leaves are studied that may be used as a basis for plant classification and recognition. These features are independent of leaf maturity and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images; then, a testing set of images is used for verifying the classification algorithms. iv ACKNOWLEDGMENTS First and foremost, I would like to express my deepest gratitude to my advisor, Dr. Rahman Tashakkori, for his continuous support and guidance throughout my M.S. pursuit. His love towards research work and innovation was the biggest motivational factor for me. He taught me to view a problem as an adventure which has led me to enjoy my research while meeting the challenges. Without his guidance and persistent help this thesis would not have been possible. I would also like to express my heartfelt thanks to my co-advisors, Dr. Alice McRae and Dr. Zack Murrell, for their valuable advice. I would like to thank my family for continually believing in me. They have always encouraged me never to give up in pursuing my goals. Without their love and presence the successes as well as the failures would have been meaningless. Finally, I would like to thank my best friend, my guide and my husband, Unnati Ojha, for always being there for me to discuss and aid me through difficult situations. Without his help, care and understanding, all of my achievements would have been unreached. v TABLE OF CONTENTS CHAPTER 1: INTRODUCTION ....................................................................................... 1 1.1. Computer Vision ........................................................................................... 1 1.2. Measurement/Extraction of Features ............................................................ 1 1.3. Pattern Classification .................................................................................... 2 1.4. Pattern Recognition ....................................................................................... 2 1.5. Applications of Computer Vision ................................................................. 3 1.6. Thesis Organization ...................................................................................... 3 CHAPTER 2: RELATED WORK ...................................................................................... 5 CHAPTER 3: IMAGE PROCESSING ............................................................................... 9 3.1. Introduction ................................................................................................... 9 3.2. Convex Hull and Convex Hull Ratio .......................................................... 10 3.3. Eccentricity ................................................................................................. 11 3.4. Isoperimetric Quotient ................................................................................ 12 3.5. Skeleton ....................................................................................................... 12 3.6. Clustering .................................................................................................... 14 CHAPTER 4: METHODOLOGY .................................................................................... 15 4.1. System Overview ........................................................................................ 15 4.2. Image Preprocessing ................................................................................... 17 4.2.1. Conversion of RGB Format to Grayscale ............................................ 17 4.2.2. Image Transformations ........................................................................ 19 4.2.3. Conversion of Grayscale Format to Binary ......................................... 19 4.2.4. Boundary Extraction ............................................................................ 20 4.3. Measurement/Extraction of Features .......................................................... 23 4.3.1. Extraction of Convex Hull Ratio ......................................................... 24 4.3.2. Extraction of Isoperimetric Quotient ................................................... 26 vi 4.3.3. Extraction of Eccentricity .................................................................... 26 4.3.4. Extraction of Aspect Ratio ................................................................... 28 4.3.5. Extraction of Skeleton ......................................................................... 28 4.4. Pattern Classification .................................................................................. 33 4.5. Pattern Recognition ..................................................................................... 36 4.5.1. Training ................................................................................................ 37 4.5.2. Testing ................................................................................................. 38 CHAPTER 5: RESULTS .................................................................................................. 39 5.1. Image Preprocessing ................................................................................... 39 5.2. Convex Hull Ratio ...................................................................................... 41 5.3. Isoperemetric Quotient ................................................................................ 42 5.4. Eccentricity and Aspect Ratio ..................................................................... 42 5.5. Skeleton ....................................................................................................... 43 5.6. Classification ............................................................................................... 48 5.7. Recognition ................................................................................................. 53 CHAPTER 6: Conclusions and Future Recommendations ............................................... 55 6.1. Conclusion .................................................................................................. 55 6.1.1. Isoperemetric Quotient ........................................................................ 55 6.1.2. Eccentricity and Aspect Ratio ............................................................. 56 6.1.3. Convex Hull Ratio ............................................................................... 56 6.1.4. Skeleton ............................................................................................... 56 6.1.5. Summary .............................................................................................. 57 6.2. Future Recommendations ........................................................................... 57 BIBLIOGRAPHY .............................................................................................................. 59 APPENDIX A…. ............................................................................................................... 61 APPENDIX B ……………………………………………………………………………68 APPENDIX C…………………………………………………………………………… 71 VITA…………………………………………………………………………………….. 75 vii LIST OF TABLES Table 4-1: Different shapes of leaves used in the classification and their features……...35 Table 5-1: Results of clustering algorithm……………………………………………….53 Table 5-2: Recognition results of the test data set…………………………………….....54 viii LIST OF FIGURES Fig. 3.1: Illustration of a convex hull ................................................................................ 10 Fig. 3.2: The best fitting ellipse to a polygon with its major and minor axes ................... 11 Fig. 3.3: a) An arbitrary object, b) and c) various positions of maximum disks with centers on skeleton of the object, d) the complete skeleton ............................... 13 Fig. 4.1: A sample plant specimen from I. W. Carpenter, Jr. Herbarium .......................... 16 Fig. 4.2: Steps involved in image preprocessing ..............................................................