Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping

Abstract

There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing the calculation of NDVI), but using approaches related with the crop physiology are rare. High-resolution hyperspectral remote sensing imagery provides optical indices related to physiological condition through the quantification of photosynthetic pigment and chlorophyll fluorescence emission. This study demonstrates the use of narrow-band indicators of stress as a potential tool for phenotyping under rainfed conditions using two airborne datasets acquired over a wheat experiment with 150 plots comprising two species and 50 varieties (bread and durum wheat). The flights were performed at the early stem elongation stage and during the milking stage. Physiological measurements made at the time of flights demonstrated that the second flight was made during the terminal stress, known to largely determine final yield under rainfed conditions. The hyperspectral imagery enabled the extraction of thermal, radiance, and reflectance spectra from 260 spectral bands from each plot for the calculation of indices related to photosynthetic pigment absorption in the visible and red-edge regions, the quantification of chlorophyll fluorescence emission, as well as structural indices related to canopy structure. Under the conditions of this study, the structural indices OPEN ACCESS Remote Sens. 2015, 7 13587(i.e., NDVI) did not show a good performance at predicting yield, probably because of the large effects of terminal water stress. Thermal indices, indices related to chlorophyll fluorescence (calculated using the FLD method), and carotenoids pigment indices (PRI and CAR) demonstrated to be better suited for screening complex traits such as crop yield. The study concludes that the indicators derived from high-resolution thermal and hyperspectral airborne imagery are efficient tools for field-based phenotyping providing additional information to standard NDVI imagery currently used.

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

“…Statistical methods are most commonly used and are based on univariate and multivariate regression models (Capolupo et al 2015). Although both regression models aim to predict crop performance traits, the univariate approach only uses a limited set of SRI (Gonzalez-Dugo et al 2015), whereas the multivariate approach utilises the entire spectrum to model plant response (Kipp et al 2014). Some authors show that, in comparison to univariate approaches, multivariate approaches were able to provide better results in detection of early stages of biotic stress (Römer et al 2011), or in the predict nitrogen and water content (Kusnierek and Korsaeth 2015).…”

Leaf Spectral Reflectance of Maize Seedlings and Its Relationship to Cold Tolerance

“…In addition, plant traits may be modeled as functions of reflectance values at multiple wavelengths. Gonzalez-Dugo et al (2015) used ridge regression of canopy reflectance to predict wheat (Triticum aestivum L.) grain yield in a large, genetically diverse panel. Montes et al (2011) used light curtains and second derivative analysis of spectral reflectance data to get a highly accurate estimate of maize biomass.…”

“…Montes et al (2011) used light curtains and second derivative analysis of spectral reflectance data to get a highly accurate estimate of maize biomass. Gonzalez-Dugo et al (2015) used ridge regression of canopy reflectance to predict wheat (Triticum aestivum L.) grain yield in a large, genetically diverse panel.…”

Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry

“…A number of systems have been developed or are under development [33][34][35][36][37][38] to address canopy traits in crops via remote sensing systems, but, to date, very few reports exist of their implementation on full scale breeding programs or genetics experiments [39]. In this paper, we will present an application case of a tractor mounted, multispectral and imaging remote sensing system to decipher the genetics of wheat response to nitrogen.…”