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Article

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

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Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, 9090 Melle, Belgium
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KU Leuven, Division of Forest, Nature and Landscape, Celestijnenlaan 200E, 3001 Leuven, Belgium
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KU Leuven, Department of Biosystems, MeBios, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
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Ghent University, Department of Plant Biotechnology and Bioinformatics, Technologiepark 927, 9052 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1644; https://doi.org/10.3390/rs12101644
Received: 28 February 2020 / Revised: 11 May 2020 / Accepted: 15 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue UAVs for Vegetation Monitoring)
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. View Full-Text
Keywords: Glycine max; RGB; canopy cover; canopy height; close remote sensing; growth model; curve fitting Glycine max; RGB; canopy cover; canopy height; close remote sensing; growth model; curve fitting
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MDPI and ACS Style

Borra-Serrano, I.; De Swaef, T.; Quataert, P.; Aper, J.; Saleem, A.; Saeys, W.; Somers, B.; Roldán-Ruiz, I.; Lootens, P. Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials. Remote Sens. 2020, 12, 1644. https://doi.org/10.3390/rs12101644

AMA Style

Borra-Serrano I, De Swaef T, Quataert P, Aper J, Saleem A, Saeys W, Somers B, Roldán-Ruiz I, Lootens P. Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials. Remote Sensing. 2020; 12(10):1644. https://doi.org/10.3390/rs12101644

Chicago/Turabian Style

Borra-Serrano, Irene, Tom De Swaef, Paul Quataert, Jonas Aper, Aamir Saleem, Wouter Saeys, Ben Somers, Isabel Roldán-Ruiz, and Peter Lootens. 2020. "Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials" Remote Sensing 12, no. 10: 1644. https://doi.org/10.3390/rs12101644

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