Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping

Abstract Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high‐throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle‐based high‐throughput phenotyping platforms (UAV‐HTPPs)...

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Autores principales:	Jinyu Wang, Xianran Li, Tingting Guo, Matthew J. Dzievit, Xiaoqing Yu, Peng Liu, Kevin P. Price, Jianming Yu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/3f5d20a5cb34478fade5b18153c138f6
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spelling	oai:doaj.org-article:3f5d20a5cb34478fade5b18153c138f62021-12-05T07:50:12ZGenetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping1940-337210.1002/tpg2.20155https://doaj.org/article/3f5d20a5cb34478fade5b18153c138f62021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20155https://doaj.org/toc/1940-3372Abstract Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high‐throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle‐based high‐throughput phenotyping platforms (UAV‐HTPPs) provide novel opportunities for large‐scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV‐based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV‐HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized‐splines (P‐splines) model was used to obtain genotype‐specific curve parameters. Genome‐wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P‐splines models revealed the dynamic change of genetic effects, indicating the role of gene–environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra‐high spatial resolution multispectral imagery, as that acquired using a UAV‐based remote sensing, for genetic dissection of NDVI.Jinyu WangXianran LiTingting GuoMatthew J. DzievitXiaoqing YuPeng LiuKevin P. PriceJianming YuWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Jinyu Wang Xianran Li Tingting Guo Matthew J. Dzievit Xiaoqing Yu Peng Liu Kevin P. Price Jianming Yu Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
description	Abstract Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high‐throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle‐based high‐throughput phenotyping platforms (UAV‐HTPPs) provide novel opportunities for large‐scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV‐based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV‐HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized‐splines (P‐splines) model was used to obtain genotype‐specific curve parameters. Genome‐wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P‐splines models revealed the dynamic change of genetic effects, indicating the role of gene–environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra‐high spatial resolution multispectral imagery, as that acquired using a UAV‐based remote sensing, for genetic dissection of NDVI.
format	article
author	Jinyu Wang Xianran Li Tingting Guo Matthew J. Dzievit Xiaoqing Yu Peng Liu Kevin P. Price Jianming Yu
author_facet	Jinyu Wang Xianran Li Tingting Guo Matthew J. Dzievit Xiaoqing Yu Peng Liu Kevin P. Price Jianming Yu
author_sort	Jinyu Wang
title	Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
title_short	Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
title_full	Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
title_fullStr	Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
title_full_unstemmed	Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
title_sort	genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/3f5d20a5cb34478fade5b18153c138f6
work_keys_str_mv	AT jinyuwang geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT xianranli geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT tingtingguo geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT matthewjdzievit geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT xiaoqingyu geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT pengliu geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT kevinpprice geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping AT jianmingyu geneticdissectionofseasonalvegetationindexdynamicsinmaizethroughaerialbasedhighthroughputphenotyping
_version_	1718372549225611264

Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping

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