ArticleThe plant genome2021
Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- Hyperspectral phenotyping and GWAS identify novel QTLs for soybean photosynthetic rate.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Comparative analysis of genomic prediction approaches for multiple time-resolved traits in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Article
- Genetic analysis of predicted vegetative biomass and biomass-related traits from digital phenotyping of strawberry.The plant genome · 2025Article
- Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.The plant genome · 2024Article
- Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location.Plant methods · 2024Article
- Modeling soybean growth: A mixed model approach.PLoS computational biology · 2024Article
- Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.Plant phenomics (Washington, D.C.) · 2024Article
- Salt tolerance evaluation and key salt-tolerant traits at germination stage of upland cotton.Frontiers in plant science · 2024Article
- Multi-Omics Techniques for Soybean Molecular Breeding.International journal of molecular sciences · 2022Review
- Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.Frontiers in plant science · 2022Article
- Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.Frontiers in plant science · 2022Article
- Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.The plant genome · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high-dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension-reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.
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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.