| Preface | 5 |
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| Contents | 9 |
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| Contributors | 19 |
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| Part I Spatial and Temporal Heterogeneity of Crops, Pests, Diseases and Weeds Causes and Implications | 25 |
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| 1 Soil Heterogeneity and Crop Growth | 26 |
| 1 Sources and Scales of Soil Heterogeneity | 26 |
| 2 Methods of Assessment | 31 |
| 3 Spatially Differentiated Crop Management | 35 |
| 4 Summary | 38 |
| References | 38 |
| 2 Spatial and Temporal Dynamics of Weed Populations | 40 |
| 1 Introduction | 40 |
| 2 Weed Mapping | 41 |
| 3 Temporal and Spatial Dynamics of Weed Populations | 42 |
| 4 Conclusions | 47 |
| References | 47 |
| 3 Spatial and Temporal Dynamics of Plant Pathogens | 49 |
| 1 Introduction | 49 |
| 2 Testing Conceptual Stimulus-Response Relationships Using GPS, GIS, and Remote Sensing | 50 |
| 3 The Unique Spectral Signature Paradigm | 54 |
| 4 Use of Satellite Imagery to Detect and Quantify Healthy Green Leaf Area Gradients (1-y) Versus Disease Gradients (y) | 54 |
| 5 Pathogen-Specific Temporal and Spatial Signatures A New Paradigm | 56 |
| 6 Detecting and Quantifying Healthy Green Leaf Area (1-y) Gradients | 57 |
| 7 Lessons Learned from the Past: Quantifying Disease and HGLA Gradients | 59 |
| 8 Quantifying Additional Temporal and Spatial Signatures for Asian Soybean Rust | 61 |
| 9 Comparison of Pathogen-Specific Temporal and Spatial Signatures to Differentiate Two Fungal Pathogens of Soybean | 63 |
| 10 Comparison of NDVI with the NIR Band to Quantify HGLA | 64 |
| 11 Implications for Plant Pathogen Forensics | 66 |
| 12 A New Paradigm for Crop Health Management | 66 |
| 13 Conclusions | 69 |
| References | 69 |
| 4 Spatial and Temporal Dynamics of Arthropods in ArableFields | 73 |
| 1 Introduction | 73 |
| 2 Field, Field Borders and Core Area | 74 |
| 3 Primary Colonization of the Field | 76 |
| 3.1 Passive Migration | 76 |
| 3.2 Active Migration | 77 |
| 4 Dispersal of Immigrants Inside the Field | 78 |
| 5 Population Build-Up and Dispersal Inside the Field | 79 |
| 6 Border Effects on Dispersal and Emigration | 80 |
| 7 Effects of the Plant Physiology | 80 |
| 8 Effects of Natural Enemies | 81 |
| 8.1 Immigration | 81 |
| 8.2 Functional and Numerical Response | 82 |
| 9 Overall Effects | 82 |
| 10 Practical Implications for Precision Farming | 82 |
| References | 83 |
| Part II Sensing and Sensor Technologies in Crop Protection | 87 |
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| 5 The Use of Laboratory Spectroscopy and Optical Remote Sensing for Estimating Soil Properties | 88 |
| 1 Introduction | 88 |
| 2 Background | 90 |
| 3 Retrieval Methods | 92 |
| 3.1 Artificial Neural Networks | 93 |
| 3.2 Partial Least Squares Modeling (PLSR, PLSR Combined with a Genetic Algorithm) | 93 |
| 3.3 Support Vector Machine Regression | 96 |
| 3.4 Penalized-Spline Signal Regression (PSR) | 96 |
| 4 Applications | 97 |
| 4.1 Scale Dependencies in the Assessment of Chemical Soil Constituents | 97 |
| 4.2 Estimation of Optically Featureless Soil Components | 101 |
| 5 Conclusions | 102 |
| References | 104 |
| 6 Sensing of Photosynthetic Activity of Crops | 107 |
| 1 Background on Optical Spectroscopy of Plant Canopies | 107 |
| 2 Remote Sensing of Photosynthesis | 109 |
| 2.1 Photochemical Reflectance Index (PRI) | 109 |
| 2.2 Fluorescence | 110 |
| 2.3 Retrieval of Remotely Measured Sun-Induced Chlorophyll Fluorescence | 111 |
| 3 Case Studies | 113 |
| 3.1 CEFLES-2 Campaign | 113 |
| 3.2 Characterization of Spatial and Species Dependent Variability of Photosynthesis Using Fluorescence Estimates | 114 |
| 4 Conclusions | 116 |
| References | 117 |
| 7 Remote Sensing for Precision Crop Protection -- A Matterof Scale | 120 |
| 1 Introduction | 120 |
| 2 The Spatial Dimension of Remote Sensing | 121 |
| 3 The Temporal Dimension of Remote Sensing | 126 |
| 3.1 The Temporal Scales of Crop Stress Phenomena | 126 |
| 3.2 The Temporal Sensor Observation Scale | 127 |
| 3.3 The Temporal Management Scale | 129 |
| 4 The Spectral Dimension of Remote Sensing | 130 |
| 4.1 Near-Range Spectroscopy for Crop Stress Detection | 131 |
| 4.2 Airborne Hyperspectral Imaging for Crop Stress Detection | 132 |
| 5 Conclusion | 134 |
| References | 135 |
| 8 Detection and Identification of Weeds | 138 |
| 1 Introduction | 138 |
| 2 Properties to Distinguish Plant Species | 139 |
| 2.1 Spectral Properties | 139 |
| 2.1.1 Remote Sensing | 142 |
| 2.1.2 Fluorescence | 142 |
| 2.2 Location and Temporal Properties | 143 |
| 2.2.1 Morphological Properties | 143 |
| 2.2.2 Overlapping | 144 |
| 2.2.3 Texture | 144 |
| 3 Image Processing for Automatic Weed Species Identification | 145 |
| 3.1 Segmentation | 145 |
| 3.2 Shape-Based Weed Discrimination | 147 |
| 3.3 Classification | 148 |
| 4 Conclusions | 150 |
| References | 151 |
| 9 Detection of Fungal Diseases Optically and Pathogen Inoculum by Air Sampling | 154 |
| 1 Introduction | 154 |
| 2 The Opportunity for Optical Detection of Disease | 155 |
| 3 Effects of Diseases on Plants | 155 |
| 4 Fusion of Optical Factors to Diagnose Diseases from Other Stresses | 158 |
| 5 Measurement Techniques | 160 |
| 5.1 Reflectance | 160 |
| 5.2 Fluorescence | 161 |
| 5.3 Thermal Radiation | 161 |
| 6 Practical Considerations for Disease Mapping | 161 |
| 7 Limitations to Precision Disease Control | 163 |
| 8 Precision Pest Management by Air Sampling | 164 |
| 9 Discussion | 165 |
