Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Material, Environments, and Growing Conditions
2.2. Ground Phenotyping Data
2.3. UAV Data Platforms, Camera Systems and UAV Campaigns
2.4. UAV Data Processing
2.4.1. Initial Pre-Processing of Multispectral Imagery
2.4.2. Photogrammetric Processing
2.4.3. Crop Height Model (CHM) and Vegetation Index (VI) Calculation
2.4.4. Soil Masking
2.4.5. Vegetation Cover (VCOV) Derivation
2.4.6. Plotwise Feature Extraction
2.5. Statistical Analysis
2.5.1. Ground Truth Validation
2.5.2. Growth Rate Modelling
2.5.3. Yield Prediction
- Approach 1: Applying a single linear regression model for each trait at a single time point
- Approach 2: Including all measured and derived traits of the same time point as predictors in a multiple regression
- Approach 3: Extending the multiple regression model across multiple time points, resulting in a multi-temporal stacked prediction
2.5.4. Genotype Association Study
3. Results
3.1. Canopy Height Determination
3.2. Vegetation Cover
3.3. Yield Prediction
4. Discussion
4.1. Canopy Height Determination
4.2. Vegetation Cover
4.3. Yield Prediction
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Trait | Abbreviation | Unit | Instrument of Determination | Measurement |
---|---|---|---|---|
Time to shooting | SHO | days | visually | Number of days from sowing until first node noticeable 1 cm above soil surface for 50% of all plants of a plot, i.e., BBCH 31 [81] |
Time to heading | HEA | days | visually | Number of days from sowing until awn emergence for 50% of all plants of a plot, i.e., BBCH 49 [81] |
Time to maturity | MAT | days | visually | Number of days from sowing until hard dough: grain content firm and fingernail impression held, BBCH 87 [81] |
Canopy height | HEIGT a/ HEICHM b/ HEICHMred c | cm | visually/ UAV data (RGB) | Average canopy height of all plants of a plot measured once a week. UAV RGB Data were used to construct digital elevation models (DEM), which led to the determination of growth parameters (HEIGRi d, HEIGRd e, HEIMAX f) |
Vegetation cover | VCOV | % | UAV data (RGB, Multispectral) | Area of a plot covered by plants, which led to the determination of growth parameters (VCOVGRi g, VCOV90 h, VCOVsmoothed i) |
Plot yield | YLD | kg | Harvester/ UAV data (RGB, Multispectral) | Grain weight harvesting the whole plot (7.5m2)/ Modelling based on UAV data, VCOV and HEI |
Index | Index Full Name | Platform | Group/Sensitivity | Formula | Reference |
---|---|---|---|---|---|
B1-NIR1 | Near infrared band 1 | Multi | Single band | - | more information see Table S4 |
B2-RED | Red band | Multi | Single band | - | |
B3-RE1 | Red edge band 1 | Multi | Single band | ||
B4-RE2 | Red edge band 2 | Multi | Single band | ||
B5-NIR2 | Near infrared band 2 | Multi | Single band | ||
B6-WA | Water band | Multi | Single band | ||
EVI2 | Enhanced vegetation index 2 | Multi | Pigments | Jiang et al. [84] | |
NDVI | Normalized difference vegetation index | Multi | Pigments | Rouse et al. [85] | |
ND-NIR1RE1 | Normalized difference NIR1-RE1 | Multi | Pigments | - | |
ND-NIR1RE2 | Normalized difference NIR1-RE2 | Multi | Pigments | - | |
ND-NIR2RED | Normalized difference NIR2-RED | Multi | Pigments | - | |
ND-NIR2RE1 | Normalized difference NIR2-RE1 | Multi | Pigments | - | |
ND-NIR2RE2 | Normalized difference NIR2-RE2 | Multi | Pigments | - | |
NDWI | Normalized difference water index | Multi | Water content | Penuelas et al. [86] | |
PSSRa | Pigment Specific Simple Ratio | Pigments | Blackburn [87] | ||
SR980_R700 | Simple Ratio | Multi | Water content | ||
RDVI | Renormalized difference vegetation index | Multi | Pigments | Roujean et al. [88] | |
RENDVI | Red edge normalized difference vegetation index | Multi | Pigments | Gitelson et al. [89] | |
REP | Red edge position | Multi | Physiology | Guyot [90] | |
RVSI | Red edge vegetation stress index | Multi | Physiology | Horler et al. [91] | |
VOG | Vogelmann ratio | Multi | Pigments | Vogelmann et al. [92] | |
B1-R | Red | RGB | Single band | - | |
B2-G | Green | RGB | Single band | - | |
B3-B | Blue | RGB | Single band | - | |
EG | Excess greenness | RGB | Pigments | Nijland et al. [44] | |
GCC | Green chromatic coordinate | RGB | Pigments | Nijland et al. [44] | |
NGRDI | Normalized green red difference index | RGB | Pigments | Zarco-Tejada et al. [93] | |
RGBVI | Red green blue vegetation index | RGB | Pigments | Bendig et al. [94] | |
TGI | Triangular greenness index | RGB | Pigments | Hunt et al. [95] | |
VARI | Visible atmospheric resistant index | RGB | Pigments | Gitelson et al. [96] |
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Herzig, P.; Borrmann, P.; Knauer, U.; Klück, H.-C.; Kilias, D.; Seiffert, U.; Pillen, K.; Maurer, A. Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding. Remote Sens. 2021, 13, 2670. https://doi.org/10.3390/rs13142670
Herzig P, Borrmann P, Knauer U, Klück H-C, Kilias D, Seiffert U, Pillen K, Maurer A. Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding. Remote Sensing. 2021; 13(14):2670. https://doi.org/10.3390/rs13142670
Chicago/Turabian StyleHerzig, Paul, Peter Borrmann, Uwe Knauer, Hans-Christian Klück, David Kilias, Udo Seiffert, Klaus Pillen, and Andreas Maurer. 2021. "Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding" Remote Sensing 13, no. 14: 2670. https://doi.org/10.3390/rs13142670