ArticlePlant phenomics (Washington, D.C.)2019
Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean.
Article in Plant phenomics (Washington, D.C.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Field-scale robotic phenotyping of three-dimensional wheat canopy architectural traits.Frontiers in plant science · 2026Article
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction.Frontiers in artificial intelligence · 2026Article
- Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.G3 (Bethesda, Md.) · 2025Article
- Near-Infrared Spectroscopy-Based Phenomics Data Can Improve Genomic Prediction of Agronomic and Grain Quality Traits Across Multi-Environment Sorghum Hybrid Trials.Plants (Basel, Switzerland) · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- GWAS-assisted and multitrait genomic prediction for improvement of seed yield and canning quality traits in a black bean breeding panel.G3 (Bethesda, Md.) · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis.Frontiers in plant science · 2025Article
- Resilience of soybean genotypes to drought stress during the early vegetative stage.Scientific reports · 2024Article
- Multi-omics assists genomic prediction of maize yield with machine learning approaches.Molecular breeding : new strategies in plant improvement · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Article
- A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications.Sensors (Basel, Switzerland) · 2023Article
- Review
- PhytoOracle: Scalable, modular phenomics data processing pipelines.Frontiers in plant science · 2023Article
- Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance.Frontiers in genetics · 2023Review
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.