Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA.
Dep. of Statistics, Iowa State Univ., Ames, IA, 50011, USA.
Plant Genome. 2021 Nov;14(3):e20155. doi: 10.1002/tpg2.20155. Epub 2021 Oct 1.
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.
在田间条件下进行植物表型分析在农业研究中起着重要作用。高效准确的高通量表型策略能够更好地将基因型与表型联系起来。基于无人机的高通量表型平台（UAV-HTPP）为大规模、高效率、高分辨率和低成本的植物性状近距测量提供了新的机会。本研究的目的是利用基于无人机的多光谱图像中提取的时间序列归一化差异植被指数（NDVI）来描述其在整个发育过程中的变化模式，并对大玉米群体中的 NDVI 进行遗传剖析。利用 UAV-HTPP 从多光谱传感器获得了 1752 个不同玉米品系在整个生长季节的五个时间点的时间序列 NDVI 数据。对采集到的测量值进行聚类分析，将 1752 个玉米品系分为两组，具有不同的 NDVI 发育趋势。为了捕捉这些静态观测背后的动态，使用惩罚样条（P-splines）模型获得基因型特异性曲线参数。使用静态 NDVI 值和曲线参数作为表型性状进行全基因组关联研究（GWAS），检测到与性状显著相关的信号。此外，使用 P-splines 模型的投影 NDVI 值进行 GWAS 揭示了遗传效应的动态变化，表明基因-环境相互作用在控制整个生长季节 NDVI 方面的作用。我们的结果表明，基于无人机的遥感获取的超高空间分辨率多光谱图像在 NDVI 的遗传剖析中具有实用性。
