ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2020
Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2020. 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.
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Article
- Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.G3 (Bethesda, Md.) · 2024Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.The plant genome · 2024Article
- Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Genetics · 2024Article
- Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.Frontiers in plant science · 2024Article
- Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling.Frontiers in plant science · 2023Article
- Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.The plant genome · 2022Article
- Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.The plant genome · 2021Article
- Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.Frontiers in plant science · 2021Article
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Authors and funding
7 authors.
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Abstract
key messageHeritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.
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