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 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。

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept at soybean(Glycine max L.(Merr.))pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot.To meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning architectures.Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions.We used data from controlled imaging environment in field,as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation.Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.

Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters

We report a root system architecture(RSA)traits examination of a larger scale soybean accession set to study trait genetic diversity.Suffering from the limitation of scale,scope,and susceptibility to measurement variation,RSA traits are tedious to phenotype.Combining 35,448 SNPs with an imaging phenotyping platform,292 accessions(replications=14)were studied for RSA traits to decipher the genetic diversity.Based on literature search for root shape and morphology parameters,we used an ideotypebased approach to develop informative root(iRoot)categories using root traits.The RSA traits displayed genetic variability for root shape,length,number,mass,and angle.Soybean accessions clustered into eight genotype-and phenotype-based clusters and displayed similarity.Genotype-based clusters correlated with geographical origins.SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits,while diverse accession could infuse useful genetic variation for these traits.Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods,enabling trait cataloging for breeding and research applications.The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

UAS-Based Plant Phenotyping for Research and Breeding Applications

Unmanned aircraft system(UAS)is a particularly powerful tool for plant phenotyping,due to reasonable cost of procurement and deployment,ease and flexibility for control and operation,ability to reconfigure sensor payloads to diversify sensing,and the ability to seamlessly fit into a larger connected phenotyping network.These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications.This paper reviews the state of the art in the deployment,collection,curation,storage,and analysis of data from UAS-based phenotyping platforms.We discuss pressing technical challenges,identify future trends in UAS-based phenotyping that the plant research community should be aware of,and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines.This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline(SNAP)

Nodules form on plant roots through the symbiotic relationship between soybean(Glycine max L.Merr.)roots and bacteria(Bradyrhizobium japonicum)and are an important structure where atmospheric nitrogen(N2)is fixed into bioavailable ammonia(NH3)for plant growth and development.Nodule quantification on soybean roots is a laborious and tedious task;therefore,assessment is frequently done on a numerical scale that allows for rapid phenotyping,but is less informative and suffers from subjectivity.We report the Soybean Nodule Acquisition Pipeline(SNAP)for nodule quantification that combines RetinaNet and UNet deep learning architectures for object(i.e.,nodule)detection and segmentation.SNAP was built using data from 691 unique roots from diverse soybean genotypes,vegetative growth stages,and field locations and has a good model fit(R2=0:99).SNAP reduces the human labor and inconsistencies of counting nodules,while acquiring quantifiable traits related to nodule growth,location,and distribution on roots.The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors,and their interactions on nodulation from an early development stage.The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars,as well as enhanced insight into the plant-Bradyrhizobium relationship.

A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

The yield of cereal crops such as sorghum(Sorghum bicolor L.Moench)depends on the distribution of crop-heads in varying branching arrangements.Therefore,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field.However,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human errors.Moreover,the process is almost infeasible for large-scale breeding plantations or experiments.Machine learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and counting.However,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious process.Here,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(R^(2)between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic annotation.In addition,we also visualize key features that the network learns.This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.