Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery
Abstract This study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispect...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.70004 |
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| author | Sahameh Shafiee Osval A. Montesinos‐López José Crossa Ingunn Burud Jon Arne Dieseth Muath Alsheikh Morten Lillemo |
| author_facet | Sahameh Shafiee Osval A. Montesinos‐López José Crossa Ingunn Burud Jon Arne Dieseth Muath Alsheikh Morten Lillemo |
| author_sort | Sahameh Shafiee |
| collection | DOAJ |
| description | Abstract This study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M‐matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi‐kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M‐matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single‐kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi‐kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single‐kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy. |
| format | Article |
| id | doaj-art-459fe116a0f1464784d94ccd84f00f68 |
| institution | OA Journals |
| issn | 2578-2703 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Phenome Journal |
| spelling | doaj-art-459fe116a0f1464784d94ccd84f00f682025-08-20T01:57:21ZengWileyPlant Phenome Journal2578-27032024-12-0171n/an/a10.1002/ppj2.70004Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagerySahameh Shafiee0Osval A. Montesinos‐López1José Crossa2Ingunn Burud3Jon Arne Dieseth4Muath Alsheikh5Morten Lillemo6Norwegian University of Life Sciences (NMBU) Ås NorwayUniversidad de Colima Colima MexicoInternational Maize and Wheat Improvement Center (CIMMYT) México‐Veracruz MexicoNorwegian University of Life Sciences (NMBU) Ås NorwayGraminor AS Ridabu NorwayNorwegian University of Life Sciences (NMBU) Ås NorwayNorwegian University of Life Sciences (NMBU) Ås NorwayAbstract This study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M‐matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi‐kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M‐matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single‐kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi‐kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single‐kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy.https://doi.org/10.1002/ppj2.70004 |
| spellingShingle | Sahameh Shafiee Osval A. Montesinos‐López José Crossa Ingunn Burud Jon Arne Dieseth Muath Alsheikh Morten Lillemo Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery Plant Phenome Journal |
| title | Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery |
| title_full | Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery |
| title_fullStr | Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery |
| title_full_unstemmed | Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery |
| title_short | Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery |
| title_sort | optimizing wheat crop performance genomic and phenomic insights into yield and days to maturity prediction using multi temporal uav imagery |
| url | https://doi.org/10.1002/ppj2.70004 |
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