Quantitative trail loci mapping of seed protein and oil composition in a diverse soybean recombinant inbred line population

Soybean [Glycine max (L.) Merr.] is an economical source of protein and oil for livestock and human consumption. Seed protein and oil are quantitatively inherited traits and commonly have a genetic by environment interaction. Genetic improvement of soybean has been negatively associated with seed protein due to an inherent inverse relationship, and positively associated with seed oil, making it difficult for breeders to improve seed yield, protein and oil simultaneously. Diverse soybean accessions are a useful source of genetic diversity and considered important to bring in novel protein and oil quantitative trait loci (QTL). The objective of this study was to identify protein and oil QTL in a diverse bi-parental recombinant inbred line (RIL) mapping population consisting of 118 F6- derived lines. These RILs along with parents and checks were grown over two years across two locations (IA and IL) in replicated tests, and one IL location in an un-replicated test. Protein and oil (%) were determined using near-infrared spectroscopy (NIR). The mapping population was genotyped using the BARCSoySNP6K BeadChip array and after quality control 843 SNP markers were used in QTL mapping. QTL mapping was done in IciMapping software. Significant differences were observed between RIL genotypes for oil and protein at each environment, and RILs with oil and protein content greater than the high protein and oil parents. QTL in linkage with oil and protein composition were identified. As expected, several QTL were only identified at individual locations due to the quantitative nature of the trait. However some stable QTL were identified across more than two environments. The QTL in this experiment were then co-localized with previous reported QTL for protein and oil to validate or propose new QTL

Development of phenomic-assisted breeding methodologies for prescriptive plant breeding, efficient cultivar testing, and genomic studies

Plant scientists are beginning to harness the capabilities of high dimensional ‘omic tools (e.g., genomic, phenomic) to usher in the era of digital agriculture to allow the usage of predictive analytics. While genomic tools have been developed to exploit high-density genetic markers for breeding decision making, a gap persists in the availability of phenomic-assisted breeding methodologies. Here we develop frameworks malleable to crop species and breeding objective to leverage complex high-dimension phenomic data using machine learning (ML) and optimization techniques for the development of data driven solutions designed to empower plant scientists to; develop prescriptive breeding solutions, improve the operation efficiency of breeding programs, and to expand the capacity of current phenotyping efforts through the use of a fine-tuned package of sensors assembled for a specific breeding objective. In this consortium of work, we show that phenomic predictors can be deployed for ML assisted prescriptive-breeding techniques for precision product placement and in turn these same phenomic predictors can be used for efficient cultivar testing (e.g., seed yield) to optimize breeding program operational efficiencies. Furthermore, phenomic sensors provided a wealth of data making this work ripe for genomic studies revealing the underlying genomic regions controlling yield predicting phenomic traits and rapid scanning of genotyped germplasm using genomic prediction. This work will allow breeders to continually optimize their breeding programs to begin fusing widely available genomic data with the upcoming capabilities of high throughput phenotyping techniques to streamline cultivar development pipelines

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

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

Computer vision and machine learning enabled soybean root phenotyping pipeline

Background Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. Results This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. Conclusions This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Development of phenomic-assisted breeding methodologies for prescriptive plant breeding, efficient cultivar testing, and genomic studies

Plant scientists are beginning to harness the capabilities of high dimensional ‘omic tools (e.g., genomic, phenomic) to usher in the era of digital agriculture to allow the usage of predictive analytics. While genomic tools have been developed to exploit high-density genetic markers for breeding decision making, a gap persists in the availability of phenomic-assisted breeding methodologies. Here we develop frameworks malleable to crop species and breeding objective to leverage complex high-dimension phenomic data using machine learning (ML) and optimization techniques for the development of data driven solutions designed to empower plant scientists to; develop prescriptive breeding solutions, improve the operation efficiency of breeding programs, and to expand the capacity of current phenotyping efforts through the use of a fine-tuned package of sensors assembled for a specific breeding objective. In this consortium of work, we show that phenomic predictors can be deployed for ML assisted prescriptive-breeding techniques for precision product placement and in turn these same phenomic predictors can be used for efficient cultivar testing (e.g., seed yield) to optimize breeding program operational efficiencies. Furthermore, phenomic sensors provided a wealth of data making this work ripe for genomic studies revealing the underlying genomic regions controlling yield predicting phenomic traits and rapid scanning of genotyped germplasm using genomic prediction. This work will allow breeders to continually optimize their breeding programs to begin fusing widely available genomic data with the upcoming capabilities of high throughput phenotyping techniques to streamline cultivar development pipelines.</p

Quantitative trail loci mapping of seed protein and oil composition in a diverse soybean recombinant inbred line population

Soybean [Glycine max (L.) Merr.] is an economical source of protein and oil for livestock and human consumption. Seed protein and oil are quantitatively inherited traits and commonly have a genetic by environment interaction. Genetic improvement of soybean has been negatively associated with seed protein due to an inherent inverse relationship, and positively associated with seed oil, making it difficult for breeders to improve seed yield, protein and oil simultaneously. Diverse soybean accessions are a useful source of genetic diversity and considered important to bring in novel protein and oil quantitative trait loci (QTL). The objective of this study was to identify protein and oil QTL in a diverse bi-parental recombinant inbred line (RIL) mapping population consisting of 118 F6- derived lines. These RILs along with parents and checks were grown over two years across two locations (IA and IL) in replicated tests, and one IL location in an un-replicated test. Protein and oil (%) were determined using near-infrared spectroscopy (NIR). The mapping population was genotyped using the BARCSoySNP6K BeadChip array and after quality control 843 SNP markers were used in QTL mapping. QTL mapping was done in IciMapping software. Significant differences were observed between RIL genotypes for oil and protein at each environment, and RILs with oil and protein content greater than the high protein and oil parents. QTL in linkage with oil and protein composition were identified. As expected, several QTL were only identified at individual locations due to the quantitative nature of the trait. However some stable QTL were identified across more than two environments. The QTL in this experiment were then co-localized with previous reported QTL for protein and oil to validate or propose new QTL.</p

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

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