Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Evaluation and Genetic Architecture of Pollination Traits for Hybrdi Wheat Seed Production in United States Great Plains Germplasm

Hybrid wheat offers a 20% yield advantage an necessary as genetic gain for grain yield has stagnated. Hybrid wheat’s commercial success will hinge on reducing seed production costs by improving wheat’s outcrossing ability. In total 19 traits were measured on the 299 genotypes in the Hard Winter Wheat Association Mapping Panel with anther extrusion, pollen 50 date, plant height, and pollination duration identified as the most important traits for hybrid wheat seed production. There was significant genotypic variation for anther extrusion, pollen 50 date, and plant height while pollination duration only had significant genotypic differences in mild temperatures with genotypes responding similarly to environmental conditions. Genotype x year interactions were significant for anther extrusion, pollen 50 date, and plant height but genotypic ranks did not change among years. Anther extrusion was not correlated with plant height. Hierarchical clustering revealed that excellent pollinators tended to be early and of short to intermediate stature. This result was thought to be due to lower heat stress in early genotypes. The measurement of pollination traits can be difficult. The development of molecular tools through association mapping can speed up the selection of high outcrossing genotypes. FarmCPU was chosen as the preferred GWAS method as it was able to identify both novel and known marker trait associations. FarmCPU revealed a novel haplotype on chromosome 2A which separated the highest and lowest scoring lines for anther extrusion and would be a target for gene pyramiding. PPD-D1a had the largest effect on pollen 50 date accounting for 20% of the total variation while also being significantly associated with plant height. Rht-B1 and Rht-D1 were most important for plant height accounting for 17% of the total variation. No significant markers were identified for pollination duration. Hybrid breeders working to improve outcrossing should incorporate the novel haplotype for increased anther extrusion, pair parents based on photoperiod and reduced height genes, and select environments with low heat stress