| 摘要 | 第6-7页 |
| Abstract | 第7-8页 |
| Dedication | 第9-10页 |
| Abbreviations | 第10-18页 |
| Chapter1:General introduction and litrature review | 第18-33页 |
| 1.1.General introduction | 第18-22页 |
| 1.1.1.Field-based phenotyping is a bottleneck for crop improvement | 第19页 |
| 1.1.2.Role of phenotyping in breeding activities | 第19-21页 |
| 1.1.3.Phenotyping in context of genetic gain | 第21-22页 |
| 1.2.High throughput phenotyping techniques | 第22-28页 |
| 1.2.1.High throughput phenotyping platforms | 第23-24页 |
| 1.2.2.Sensors deployed on UAVs | 第24-26页 |
| 1.2.2.1.Digital RGB Camera | 第24页 |
| 1.2.2.2.Multispectral sensors | 第24-25页 |
| 1.2.2.3.Thermal Infrared Imaging Sensor | 第25页 |
| 1.2.2.4.LIDAR | 第25-26页 |
| 1.2.3.UAV based traits | 第26-28页 |
| 1.2.3.1.Morphological traits | 第26页 |
| 1.2.3.2.Spectral reflectance based physiological traits | 第26-27页 |
| 1.2.3.3.Crop stress detection and yield prediction | 第27-28页 |
| 1.3.High throughput phenotyping for genetic gain | 第28-30页 |
| 1.3.1.Increasing throughput and accuracy in phenotyping | 第28-29页 |
| 1.3.2.Affordable phenotyping systems | 第29-30页 |
| 1.3.3.Quality of field trials | 第30页 |
| 1.4.Objectives and rationale of the study | 第30-32页 |
| 1.5.Method layout of the study | 第32-33页 |
| Chapter2:Rapid monitoring of NDVI across the wheat growth cycle for phenotypic selection and grain yield prediction | 第33-51页 |
| 2.1.Introduction | 第34-35页 |
| 2.2 Material and Methods | 第35-39页 |
| 2.2.1.Germplasm and field trials | 第35-36页 |
| 2.2.2.Multi-spectral platform and imagery campaign | 第36-37页 |
| 2.2.3.Orthomosaic generation,segmentation and extraction of pixel values | 第37-38页 |
| 2.2.4.Estimation of UAV-NDVI,NDRE and NGRDI | 第38页 |
| 2.2.5.Ground data collection and statistical analysis | 第38-39页 |
| 2.3 Results | 第39-47页 |
| 2.3.1.Assessment of UAV platform data accuracy | 第39-41页 |
| 2.3.2.Correlations between vegetative indices,biomass and grain yield | 第41-45页 |
| 2.3.3.Accuracy of UAV-platform for yield prediction | 第45页 |
| 2.3.4.Assessment of water treatments impact from UAV-NDVI | 第45-47页 |
| 2.4 Discussion | 第47-50页 |
| 2.4.0.Comparison of UAV and Greenseeker | 第47页 |
| 2.4.1.UAV-data accuracy for biomass and yield prediction | 第47-48页 |
| 2.4.2.Significance of NDVI monitoring for yield prediction | 第48-49页 |
| 2.4.3.Detection of drought effect on NDVI and yield | 第49-50页 |
| 2.5.Conclusions | 第50-51页 |
| Chapter3:Time-series multispectral indices from UAV-based imagery reveal senescence rate in bread wheat | 第51-70页 |
| 3.1.Introduction | 第52-53页 |
| 3.2.Material and Methods | 第53-58页 |
| 3.2.1.Germplasm | 第53页 |
| 3.2.2.Experimental design | 第53-54页 |
| 3.2.3.UAV platform and flight mission | 第54-55页 |
| 3.2.4.Data acquisition Schedule | 第55页 |
| 3.2.5.Image processing and data extraction | 第55-56页 |
| 3.2.6.Estimation of narrowband SVIs and senescence rate | 第56-57页 |
| 3.2.7.Collection of ground morphological data | 第57页 |
| 3.2.8.Statistical analysis | 第57-58页 |
| 3.3.Results | 第58-67页 |
| 3.3.1.Accuracy of SVI data to predict growth status | 第58-62页 |
| 3.3.2.Estimates of variance components and correlations among SVIs and yield traits | 第62-63页 |
| 3.3.3.Dynamics and interaction of SVIs during different growth stages | 第63-65页 |
| 3.3.4.Impact of senescence rate on yield and performance of genotypes | 第65-67页 |
| 3.4.Discussion | 第67-69页 |
| 3.4.1.Validation of SVI Data at different growth stages | 第67页 |
| 3.4.2.Genotypic variation and traits correlation | 第67-68页 |
| 3.4.3.Dynamics of SVIs explained interaction between growth stages | 第68页 |
| 3.4.4.Impact of senescence rate on yield | 第68-69页 |
| 3.4.5.Cultivars with low senescence rate | 第69页 |
| 3.5.Conclusions | 第69-70页 |
| Chapter4:Accuracy assessment of UAV-based plant height for quantitative genomic analysis in bread wheat | 第70-85页 |
| 4.1.Introduction | 第71-72页 |
| 4.2.Material and Methods | 第72-76页 |
| 4.2.1.Germplasm and experimental design | 第72页 |
| 4.2.2.Remote sensing campaign,mosaicking and DSM generation | 第72-73页 |
| 4.2.3.DSM evaluation and plant height model(PHM)development | 第73-74页 |
| 4.2.4.Estimation and validation of UAV-based plant heights | 第74-75页 |
| 4.2.5.SNP genotyping,QTL analysis and genomic prediction | 第75-76页 |
| 4.2.6.Statistical analysis | 第76页 |
| 4.3 Results | 第76-81页 |
| 4.3.1.Accuracy assessment of UAV-based plant height | 第76-78页 |
| 4.3.2.Phenotypic variation | 第78页 |
| 4.3.3.Identification of QTL and their impact on phenotype | 第78-79页 |
| 4.3.4.Validation of UAV-based QTL results | 第79-80页 |
| 4.3.5.Genomic prediction accuracy of UAV-based data set | 第80-81页 |
| 4.4.Discussion | 第81-83页 |
| 4.4.1.Accuracy and phenotypic variations in UAV-based plant height | 第81-82页 |
| 4.4.2.UAV-based QTLs and their effects on phenotype | 第82-83页 |
| 4.4.3.Accuracy of UAV-base QTL | 第83页 |
| 4.4.4 Accuracy of UAV-bead plant height for genomic prediction | 第83页 |
| 4.5 Conclusions | 第83-85页 |
| Chapter5.General discussion and concluding remarks | 第85-89页 |
| References | 第89-101页 |
| Addendix | 第101-107页 |
| Acknoweledgement | 第107-108页 |
| CURRICULUM VITAE | 第108-110页 |
| 附件 | 第110-112页 |
