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

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 assess...

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Autores principales: Tolley, Seth A., Carpenter, Neal, Crawford, Melba M., Delp, Edward J., Habib, Ayman, Tuinstra, Mitchell R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318590/
https://www.ncbi.nlm.nih.gov/pubmed/37409309
http://dx.doi.org/10.3389/fpls.2023.1202536
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author Tolley, Seth A.
Carpenter, Neal
Crawford, Melba M.
Delp, Edward J.
Habib, Ayman
Tuinstra, Mitchell R.
author_facet Tolley, Seth A.
Carpenter, Neal
Crawford, Melba M.
Delp, Edward J.
Habib, Ayman
Tuinstra, Mitchell R.
author_sort Tolley, Seth A.
collection PubMed
description 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.
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spelling pubmed-103185902023-07-05 Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling Tolley, Seth A. Carpenter, Neal Crawford, Melba M. Delp, Edward J. Habib, Ayman Tuinstra, Mitchell R. Front Plant Sci Plant Science 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. Frontiers Media S.A. 2023-06-20 /pmc/articles/PMC10318590/ /pubmed/37409309 http://dx.doi.org/10.3389/fpls.2023.1202536 Text en Copyright © 2023 Tolley, Carpenter, Crawford, Delp, Habib and Tuinstra https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Tolley, Seth A.
Carpenter, Neal
Crawford, Melba M.
Delp, Edward J.
Habib, Ayman
Tuinstra, Mitchell R.
Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
title Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
title_full Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
title_fullStr Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
title_full_unstemmed Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
title_short Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
title_sort row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318590/
https://www.ncbi.nlm.nih.gov/pubmed/37409309
http://dx.doi.org/10.3389/fpls.2023.1202536
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