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

Parmley, Kyle A.; Nagasubramanian, Koushik; Sarkar, Soumik; Ganapathysubramanian, Baskar; Singh, Asheesh K.

doi:10.34133/2019/5809404

Cited by 70 publications

(60 citation statements)

References 83 publications

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“…These abovementioned solutions will help integrate multitrait objective functions [ 81 ] for above and below ground trait selections for furthering genetic gains. There is a need to deploy RSA traits in prescriptive cultivar development [ 82 ] and continue to explore and identify trait predictors for phenomics-assisted breeding [ 83 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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 ideotype-based 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Combination of spectral wavebands and other predictor traits with ML-based analytics has shown utility in crop yield and physiological trait measurement and prediction [ 157 , 158 ]. Similarly, integration of crop, genetic, and weather parameters shows usefulness in crop yield prediction using deep learning [ 159 ].…”

Section: Uas-based Imaging Of Plant Traitsmentioning

confidence: 99%

UAS-Based Plant Phenotyping for Research and Breeding Applications

et al. 2021

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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.

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“…Additionally, we demonstrated the potential of the pipeline to capture RSA trait diversity on three selected soybean genotypes, which can be expanded to larger genotype set. HTP methods together with phenomics and data analytics [120] will give researchers the tools needed to decipher the genetics of RSA trait expression to realize the potential of root driven breeding. Further work is needed to develop methods for 3D reconstruction, as well as methodologies to link and reduce the gap between controlled and field experiment root studies.…”

Section: Discussionmentioning

confidence: 99%

Computer vision and machine learning enabled soybean root phenotyping pipeline

et al. 2020

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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 autoencoder (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.

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

Cited by 70 publications

References 83 publications

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

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

UAS-Based Plant Phenotyping for Research and Breeding Applications

Computer vision and machine learning enabled soybean root phenotyping pipeline

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