Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling.

ABSTRACT: 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) hyperspectra

SUBMITTER: Tolley SA 

PROVIDER: S-EPMC10318590 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature