ArticlePlant phenomics (Washington, D.C.)2020
Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Association mapping for canopy temperature under irrigated and rainfed conditions at 12 environments identified both known and novel genetic loci.The plant genome · 2026Article
- Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean.Plant phenome journal · 2024Article
- Genome-Wide Association study for root system architecture traits in field soybean [Glycine max (L.) Merr.].Scientific reports · 2024Article
- Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity.Plant cell reports · 2024Review
- Genome-wide association studies of root system architecture traits in a broad collection ofFrontiers in plant science · 2024Article
- Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference.Frontiers in plant science · 2024Article
- VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin.Plants (Basel, Switzerland) · 2023Article
- A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images.Plant phenomics (Washington, D.C.) · 2023Article
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.Frontiers in plant science · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- OMICS in Fodder Crops: Applications, Challenges, and Prospects.Current issues in molecular biology · 2022Review
- Parsimonious root systems and better root distribution can improve biomass production and yield of soybean.PloS one · 2022Article
- Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.).Plant phenomics (Washington, D.C.) · 2022Article
- Optimizing Crop Water Use for Drought and Climate Change Adaptation Requires a Multi-Scale Approach.Frontiers in plant science · 2022Review
- Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment.Frontiers in plant science · 2022Article
- Characterization of Root System Architecture Traits in Diverse Soybean Genotypes Using a Semi-Hydroponic System.Plants (Basel, Switzerland) · 2021Article
- Advances in "Omics" Approaches for Improving Toxic Metals/Metalloids Tolerance in Plants.Frontiers in plant science · 2021Review
- Crop yield prediction integrating genotype and weather variables using deep learning.PloS one · 2021Article
- Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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