ReviewFrontiers in plant science2020
Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.
Review in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Improving multi-trait genomic prediction using synthetic traits from hyperspectral data based on co-heritability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Seasonal Variation and Genetic Evaluation of Needle Catechin Content in Half-Sib Families ofPlants (Basel, Switzerland) · 2026Article
- Persistent and transient QTLs underlying growth trajectory of plant height in sorghum.Journal of experimental botany · 2026Article
- Beyond Data: Artificial intelligence, knowledge graphs, and the next revolution in wheat breeding.Plant communications · 2026Review
- A leaf phenomics approach for estimating belowground traits in North American licorice.American journal of botany · 2026Article
- Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model.Plant phenomics (Washington, D.C.) · 2026Article
- Longitudinal genomic prediction of cacao disease resilience identifies robust witches' broom disease targets.Frontiers in plant science · 2026Article
- UAV-LiDAR high-throughput time-series phenotyping and genome-wide association analysis reveal the genetic basis of plant height in peanut (Plant phenomics (Washington, D.C.) · 2025Article
- Genomewide association and prediction of phenotypic stability in barley.The plant genome · 2025Article
- Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield.The plant genome · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Genotyping strategies for single-step genomic predictions in a simulated sheep population under different scenarios of pedigree error types.Frontiers in genetics · 2025Article
- Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2024Article
- Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Multi-omics assists genomic prediction of maize yield with machine learning approaches.Molecular breeding : new strategies in plant improvement · 2024Article
- Enhancing genomic association studies in slash pine through close-range UAV-based morphological phenotyping.Forestry research · 2024Article
- Development and applications of metabolic models in plant multi-omics research.Frontiers in plant science · 2024Review
- Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.Frontiers in plant science · 2023Article
- Genome-Wide Association Analysis of Senescence-Related Traits in Maize.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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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.