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

Irene Borra-Serrano,Peter Lootens,Tom De Swaef,Aamir Saleem,Ben Somers,Wouter Saeys,Jonas Aper,Paul Quataert,Isabel Roldán-Ruiz

doi:10.3390/rs12101644

Irene Borra-Serrano, Peter Lootens + Show 7 more

Open Access

https://doi.org/10.3390/rs12101644

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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.

Highlights

It has been demonstrated that close remote sensing approaches in combination with appropriate experimental designs and data integration can increase accuracy, precision, and throughput of on-field phenotyping experiments while reducing cost and labor requirements [1]
90.4% fits for canopy cover (CC) and 99.4% fits for canopy height (CH) reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen
In an exploratory data analysis of CC and CH data, after discarding plots with emergence

Summary

Introduction

It has been demonstrated that close remote sensing approaches in combination with appropriate experimental designs and data integration can increase accuracy, precision, and throughput of on-field phenotyping experiments while reducing cost and labor requirements [1]. These high throughput phenotyping approaches can be implemented in crop breeding to determine architectural/morphological and physiological traits to aid early detection of desirable genotypes [2]. Canopy height (CH) is dynamic and linked to growth, crop development, and yield [9,10]

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