Computational Botany (eBook)

Contents 6
1 Introduction ?????????????? 8
1.1 Plant Species and Their Identification 8
1.2 The Delimitation of Species by Descriptions 12
1.3 Using Leaves to Diagnose Species 13
1.4 Computational Botany 15
1.5 Aims and Objectives 16
2 Morphometrics: A Brief Review ?????????????? 17
2.1 Historical Background to Morphometrics 18
2.1.1 Phase 1: Classical Taxonomy, Evolutionary Theory and Biometry: The Background for Morphometrics 18
2.1.2 Phase 2: Genetics and Statistical Methods in Evolution, Agronomy and Biosystematics 20
2.1.3 Phase 3: Multivariate Statistics and Morphometrics 21
2.1.4 Phase 4: Geometric Morphometrics 23
2.2 Morphometric Analysis of Leaves 25
2.2.1 Analysis of Conventional Botanical Descriptors 26
2.2.2 Analysis of Leaf Outline Shape 27
2.2.3 Analysis of Leaf Margin Patterns 29
2.2.4 Analysis of Homologous Landmarks and Semi-landmark Configurations 29
2.2.5 Fractal Dimensions and Polygon Fitting 30
2.2.6 Analysis of Leaf Venation Patterns 30
2.2.7 Analysis of Leaf Texture 31
2.2.8 Analysis of Other Features of the Leaf Blade 31
2.3 Morphometrics of Flowers and Other Plant Organs 31
2.4 Applications 33
2.4.1 General-Purpose Species Identification 33
2.4.2 Agriculture 35
2.4.3 Intraspecific Variation, Geographical Distribution, Climate, Phylogeny 36
2.5 Summary 37
3 Feature Extraction ?????????????? 39
3.1 Leaf Shape 39
3.1.1 Study of Existing Techniques for Leaf Shape Analysis 40
3.1.2 Evaluation and Results 43
3.2 Leaf Texture 44
3.2.1 Macro-texture 45
3.2.2 Micro-texture 46
3.3 Margin Characteristics 50
3.3.1 Extracting the Margin 51
3.4 Locating the Apex and Insertion Point 53
3.4.1 Dynamic Time Warping 53
3.4.2 Finding the Points of Margin Symmetry 55
3.5 Venation Patterns 55
3.5.1 Extraction by Evolved Vein Classifiers 56
3.5.2 Extraction by Ant Colonies 59
3.5.3 Results and Comparison of Methods 61
3.6 Summary 62
4 Machine Learning for Plant Leaf Analysis ?????????????? 63
4.1 Incorporating Intra-species Variation into Plant Classification 63
4.1.1 Utilizing the Hungarian Algorithm for Improved Classification of Leaf Blades 64
4.1.2 Comparing Leaf Margins Using Dynamic Time Warping 74
4.2 Combining Different Leaf Features 75
4.2.1 Probabilistic Classification from K-Nearest-Neighbour 76
4.2.2 Automatic Feature Selection 79
4.3 Summary 84
5 Botanists' Vision ?????????????? 86
5.1 Comparing the Eye Movements of Botanists and Non-Botanists 87
5.1.1 Results and Analysis 88
5.2 Reverse Engineering of Expert Visual Observations 91
5.2.1 Related Work 92
5.2.2 Methodology 93
5.2.3 Evaluation 97
5.2.4 Summary 100
References ?????????????? 101
Index 114