Abstract
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 fusing ML 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.
Highlights
Soybean [Glycine Max (L.) Merr.] breeding programs have improved the crop genetic potential, while producers have modified their agronomic methods to increase seed yield (SY) [1,2,3,4,5]
Breeders and geneticists aim to utilize previously unused genetic accessions in cultivar development, and phenomicassisted breeding approaches have the potential to enhance the integration of genetic diversity in most mainstream programs [36]
For improving SY using diverse accession, as a first step, there is a need to establish the relationship between phenomic traits with SY using high-throughput phenotyping techniques and advanced data analytics including machine learning [9]
Summary
Soybean [Glycine Max (L.) Merr.] breeding programs have improved the crop genetic potential, while producers have modified their agronomic methods to increase seed yield (SY) [1,2,3,4,5]. Studies have shown high correlation between phenomic traits collected using digital sensors and manually collected measurements [8, 9] suggesting phenomic data can be acquired on a wide spatiotemporal scale by leveraging the technological advancements made in sensor technology with ground and aerial-based phenotyping platforms [10]. Empowered with phenomic data that was previously difficult or impossible to collect across an expansive spatiotemporal scale, scientists have begun disentangling the genetic architecture of traits through genomic studies [8, 11, 12], prediction of target trait performance using genomic [13,14,15,16], and phenomic prediction strategies [9, 15, 17,18,19,20]. Increasing soybean seed yield and on-farm profitability is the primary objective of soybean breeding programs making seed yield an important trait to target in both cultivar and germplasm breeding efforts utilizing phenomics tools that can lead to reduced environmental and genotype testing
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