The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques,which is now a critical component of mainstream cultivar development pipelines.However,advanceme...The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques,which is now a critical component of mainstream cultivar development pipelines.However,advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs.Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements.Random forest,a machine learning(ML)algorithm,was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation(CV)scenarios consistent with breeding challenges.To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives,feature importance in tandem with a genetic algorithm(GA)technique allowed selection of a subset of phenotypic traits,specifically optimal wavebands.The results illuminated the capability of fusingML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping(e.g.,seed yield harvest).While we illustrate with soybean,this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective。展开更多
基金We thank Iowa Soybean Association and Monsanto Chairin Soybean Breeding,R F Baker Center for Plant Breeding and Plant Sciences Institute at lowa State University,for financial support.
文摘The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques,which is now a critical component of mainstream cultivar development pipelines.However,advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs.Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements.Random forest,a machine learning(ML)algorithm,was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation(CV)scenarios consistent with breeding challenges.To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives,feature importance in tandem with a genetic algorithm(GA)technique allowed selection of a subset of phenotypic traits,specifically optimal wavebands.The results illuminated the capability of fusingML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping(e.g.,seed yield harvest).While we illustrate with soybean,this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective。
