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Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials. / Borra-Serrano, Irene; Swaef, Tom De; Quataert, Paul; Aper, Jonas; Saleem, Aamir; Saeys, Wouter; Somers, Ben; Roldán-Ruiz, Isabel; Lootens, Peter.

In: Remote Sensing 2020, Vol. 12, Page 1644, Vol. 12, Nr. 10, 20.05.2020, blz. 1644.

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@article{fdca0ba040854072ac62e828b6a775d7,
title = "Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials",
abstract = "Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4{\%} fits for CC and 99.4{\%} fits for CH reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R2 = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits.",
keywords = "<i>Glycine max</i>, RGB, canopy cover, canopy height, close remote sensing, curve fitting, growth model",
author = "Irene Borra-Serrano and Swaef, {Tom De} and Paul Quataert and Jonas Aper and Aamir Saleem and Wouter Saeys and Ben Somers and Isabel Rold{\'a}n-Ruiz and Peter Lootens",
year = "2020",
month = "5",
day = "20",
doi = "10.3390/RS12101644",
language = "English",
volume = "12",
pages = "1644",
journal = "Remote Sensing 2020, Vol. 12, Page 1644",
number = "10",

}

RIS

TY - JOUR

T1 - Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials

AU - Borra-Serrano, Irene

AU - Swaef, Tom De

AU - Quataert, Paul

AU - Aper, Jonas

AU - Saleem, Aamir

AU - Saeys, Wouter

AU - Somers, Ben

AU - Roldán-Ruiz, Isabel

AU - Lootens, Peter

PY - 2020/5/20

Y1 - 2020/5/20

N2 - Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R2 = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits.

AB - Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R2 = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits.

KW - <i>Glycine max</i>

KW - RGB

KW - canopy cover

KW - canopy height

KW - close remote sensing

KW - curve fitting

KW - growth model

UR - https://www.mdpi.com/721690

U2 - 10.3390/RS12101644

DO - 10.3390/RS12101644

M3 - A1: Web of Science-article

VL - 12

SP - 1644

JO - Remote Sensing 2020, Vol. 12, Page 1644

JF - Remote Sensing 2020, Vol. 12, Page 1644

IS - 10

ER -