ArticleThe plant genome2022
Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.
Article in The plant genome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions.Environmental microbiome · 2026Article
- Integration of proxy intermediate omics traits into a nonlinear two-step model for accurate phenotypic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction.Plants (Basel, Switzerland) · 2025Article
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- Possibilities of Using a Multispectral Camera to Assess the Effects of Biostimulant Application in Soybean Cultivation.Sensors (Basel, Switzerland) · 2025Article
- Modeling soybean growth: A mixed model approach.PLoS computational biology · 2024Article
- Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Genetics · 2024Article
- Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat.Molecular breeding : new strategies in plant improvement · 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
- Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.Frontiers in plant science · 2023Article
- Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.The plant genome · 2022Article
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Authors and funding
13 authors.
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Abstract
Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.
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