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

Autor: Tom De Swaef, Jonas Aper, Isabel Roldán-Ruiz, Irene Borra-Serrano, Aamir Saleem, Ben Somers, Peter Lootens, Paul Quataert, Wouter Saeys
Rok vydání: 2020
Předmět:
Zdroj: Remote Sensing, Vol 12, Iss 1644, p 1644 (2020)
Remote Sensing; Volume 12; Issue 10; Pages: 1644
Remote Sensing
ISSN: 2072-4292
DOI: 10.3390/rs12101644
Popis: 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.
Databáze: OpenAIRE
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