Tolley Seth A, Carpenter Neal, Crawford Melba M, Delp Edward J, Habib Ayman, Tuinstra Mitchell R
Department of Agronomy, Purdue University, West Lafayette, IN, United States.
Analytics and Pipeline Design, Bayer Crop Science, Chesterfield, MO, United States.
Front Plant Sci. 2023 Jun 20;14:1202536. doi: 10.3389/fpls.2023.1202536. eCollection 2023.
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
遥感技术能够快速评估许多性状，这些性状在整个生长季节为植物育种者提供有价值的信息，以提高遗传增益。这些性状通常是从行段（小区内的行）基础上的遥感数据中提取的，从而能够对小区内任何按行划分的植物子集进行定量评估，而不是像基于田间表型分析那样只对少数几个具有代表性的个体植物进行评估。然而，分析中应包括哪些行仍存在争议。本实验的目的是在使用四行小区进行的田间试验中评估行选择和小区修剪，这些小区的遥感性状是从RGB（红-绿-蓝）、LiDAR（光探测与测距）和VNIR（可见近红外）高光谱数据中提取的。在2018年至2021年的整个生长季节进行了无人机飞行，收集了三年高粱试验和两年玉米试验的数据。基于所有四个行段（RS）（RS1234）、内两行（RS23）、外两行（RS14）和单个行（RS1、RS2、RS3和RS4）从每个小区提取性状。另外还测试了40厘米的小区端部修剪这一因素。利用季末产量的重复性和预测模型来评估这些方法的性能。从未发现小区修剪与未修剪小区的结果有显著差异。基于行选择的差异经常观察到显著差异。具有更多行段的小区通常有利于提高重复性，排除外两行可改善预测模型。这些结果支持了农学实验设计的长期原则，在纳入遥感技术的育种计划中应予以考虑。
