n Johnson, Seth Murray, Jacob Washburn, Filipe I. Matias, Annarita Marrano, and Felipe Sabadin for their help and suggestions on the image processing pipeline and the research more broadly. Data availability This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image
Open resource ↗10.25739/erxg-yn49 · lines:427-461Unverified paper record
Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize
Genetics · 1 May 2024 · 10.1093/genetics/iyae037
Abstract
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
Plant phenotyping relevance
UAVマルチスペクトル画像から得た縦断的NDVIを植物・圃場プロットの表現型として用い、空間・時系列モデリング手法を提案・評価しており、表現型取得と解析ワークフローが研究の中心です。
abstractour study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery.
abstractThe normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed.
abstractIn summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
Code and data availability
The paper's maize field phenotype datasets (2015, 2017, 2019, 2020 G2F hybrid experiments) are publicly available via G2F DOIs. The genotypic SNP dataset DOI was excluded as molecular omics data; image data are said to be in the supplement but without a public URL.
e I. Matias, Annarita Marrano, and Felipe Sabadin for their help and suggestions on the image processing pipeline and the research more broadly. Data availability This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are ava
Open resource ↗10.25739/w560-2114 · lines:427-461adin for their help and suggestions on the image processing pipeline and the research more broadly. Data availability This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are available in the Supplemental section of this ma
Open resource ↗10.25739/t651-yy97 · lines:427-461processing pipeline and the research more broadly. Data availability This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are available in the Supplemental section of this manuscript. Supplemental material available at GENE
Open resource ↗10.25739/hzzs-a865 · lines:427-461This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.