Advances in plant phenotyping for more sustainable crop production

목차 (Table of Contents)

• CONTENTS
• Series list = xi
• Introduction = xix
• Acknowledgement = xxiii
• Part 1 The development of phenotyping as a research field
• 1 Origins and drivers of crop phenotyping = 3
• 1 Introduction = 3
• 2 Technological progress in plant phenotyping = 6
• 3 Community integration in plant phenotyping = 9
• 4 Plant phenotyping as a tool for enhanced and sustainable crop production = 14
• 5 Future trends = 19
• 6 Where to look for further information = 20
• 7 Acknowledgements = 21
• 8 References = 21
• 2 The evolution of trait selection in breeding : from seeing to remote sensing = 29
• 1 Introduction = 29
• 2 Selection of progeny and large-scale genetic resources = 35
• 3 Characterization of parents and gene discovery panels : increasing throughput with sensors = 38
• 4 Traits related to spike fertility and partitioning to yield = 40
• 5 Traits to improve lodging resistance in cereals = 44
• 6 Selecting for disease resistance = 47
• 7 How might trait selection look in the future = 49
• 8 Where to look for further information = 51
• 9 References = 52
• Part 2 Sensor types
• 3 Advances in optical analysis for crop phenotyping = 69
• 1 Introduction = 69
• 2 Popular optical sensors = 71
• 3 Major challenges in optical sensing = 77
• 4 Case studies = 82
• 5 Summary and future trends = 85
• 6 Where to look for further information = 87
• 7 References = 88
• 4 Advances in the use of thermography in crop phenotyping = 99
• 1 Introduction = 99
• 2 Foundational theory of thermography = 100
• 3 Principles of thermography measurement = 102
• 4 Technologies available and thermography methods = 103
• 5 Traits measured = 108
• 6 Case studies = 110
• 7 Main challenges = 113
• 8 Summary and future trends = 114
• 9 Where to look for further information = 115
• 10 References = 116
• 5 Advances in the use of X-ray computed tomography in crop phenotyping = 123
• 1 Introduction = 123
• 2 X-ray sources = 124
• 3 Interaction of X-rays with material = 127
• 4 Detector = 130
• 5 Computed tomography systems for crop phenotyping = 131
• 6 From sensor to data = 135
• 7 Case studies : Phenotyping using computed tomography = 137
• 8 Summary and future trends = 141
• 9 Where to look for further information = 142
• 10 References = 143
• Part 3 Carrier/delivery systems
• 6 Field robots for plant phenotyping = 153
• 1 Introduction = 153
• 2 Specific challenges associated with field robots = 157
• 3 Currently available field robots tor phenotyping = 158
• 4 Sensors and technologies for phenotyping field robots = 161
• 5 Robotic arms for fruit phenotyping and harvesting = 165
• 6 Conclusion and future trends = 166
• 7 Where to look for further information = 168
• 8 References = 169
• 7 Advances in high-throughput crop phenotyping using unmanned aerial vehicles (UAVs) = 179
• 1 Introduction = 179
• 2 Remote sensing tools : unmanned aerial vehicles and flight protocols 180
• 3 Major plant traits that can be extracted using unmanned aerial vehicle remote sensing = 182
• 4 Conclusion and future trends = 191
• 5 Authors' contributions = 192
• 6 Acknowledgements = 192
• 7 References = 192
• Part 4 Data analysis
• 8 Meeting computer vision and machine learning challenges in crop phenotyping = 203
• 1 Introduction = 203
• 2 Key dimensions to consider in computer vision applications in plant phenotyping = 204
• 3 Creating synergies between research communities : the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop = 208
• 4 Data challenges to accelerate progress in computer vision techniques : leaf counting and segmentation = 209
• 5 Recent agriculture-related computer vision challenges = 211
• 6 Summary = 215
• 7 Where to look for further information = 217
• 8 References = 218
• 9 Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops = 223
• 1 Introduction = 223
• 2 Digital phenotyping as a tool to support breeding programs = 226
• 3 Genotype-to-phenotyping (G2P) models : integrating data from phenomics and envirotyping in predictive breeding = 236
• 4 Conclusion = 246
• 5 Acknowledgements = 247
• 6 Whereto look for further information = 247
• 7 Abbreviations = 248
• 8 References = 249
• 10 The role of crop growth models in crop improvement : integrating phenomics, envirotyping and genomic prediction = 263
• 1 Introduction = 263
• 2 Crop growth models to understand gene xenvironmentx management interactions = 264
• 3 The role of crop simulation modelling in envirotyping = 265
• 4 The role of crop models in defining phenotyping methods and targets = 268
• 5 Crop models of the future : how can they gain from the current developments in phenotyping? = 272
• 6 Integrating statistical genetic models and crop growth models (SGM-CGM) = 273
• 7 Where to look for further information = 276
• 8 References = 276
• Part 5 Case studies
• 11 Using phenotyping techniques to analyse crop functionality and photosynthesis = 285
• 1 Introduction = 285
• 2 Understanding photosynthesis and its relationship to crop growth and stress response = 286
• 3 Phenotyping photosynthesis in varying environmental conditions = 289
• 4 Using gas exchange to analyse photosynthesis = 292
• 5 Using porometry and thermal imaging of gs and hyperspectral techniques = 301
• 6 Using chlorophyll fluorescence = 302
• 7 Photosynthesis and climate change : accounting for heat stress, drought stress and elevated CO2 = 308
• 8 Case studies = 310
• 9 Conclusions = 314
• 10 Whereto look for further information = 315
• 11 References = 316
• 12 Using phenotyping techniques to predict and model grain yield : translating phenotyping into genetic gain = 325
• 1 Introduction = 325
• 2 Boosting genetic gain in grain yield by focusing on phenomics = 327
• 3 Stomatai conductance = 335
• 4 Functional stay green = 336
• 5 Case study = 337
• 6 Conclusion and future trends = 340
• 7 Where to look for further information = 341
• 8 References = 341
• 13 Automated assessment of plant diseases and traits by sensors : how can digital technologies support smart farming and plant breeding? = 351
• 1 Introduction = 351
• 2 Digital plant disease detection = 352
• 3 Complexity of host-pathogen interactions = 355
• 4 Complexity in a crop stand = 358
• 5 Case study : application of deep learning to foliar plant diseases = 359
• 6 Summary = 364
• 7 Future trends in research = 366
• 8 Where to look for further information = 367
• 9 Acknowledgements = 367
• 10 References = 367
• Index = 373