Abstract
Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.
Highlights
Wheat is one of the most important cereals in the human diet worldwide
Despite the present study being conducted in a single year, it generated an interesting database for testing approximation methodologies. This was due to the use of a large number of cultivars/advanced lines of wheat that were grown under two contrasting hydric conditions and evaluated for a large number of traits, with spectral reflectance assessed at two developmental stages (AN and grain filling (GF))
Partial Least Square Regression (PLSR), which is the most popular technique used in studies of this kind, has the ability to reduce the effect of the spectral signatures collinearity through a reduction in the dimensionality of the data (Hastie et al, 2005); our results show that spectral reflectance indices (SRIs) may perform or better, when used in plant phenotyping
Summary
Wheat is one of the most important cereals in the human diet worldwide. This cereal is consumed in different types of processed foods, providing around 20% of total daily calories (Shewry, 2009). The current effects of climate change on weather patterns, and unexpected events, are threatening maximum thresholds in many areas (Ayeneh et al, 2002; Azimi et al, 2010; Rebetzke et al, 2012; Hernández-Barrera et al, 2016). This challenging scenario should encourage wheat breeders to accelerate the development and release of new high-yield cultivars that are adapted to more complex environmental conditions (Velu and Prakash, 2013). One strategy for improving and expediting the selection of these elite genotypes is the acquisition of high-dimensional phenotypic data (highthroughput phenotyping) (Bowman et al, 2015; Camargo and Lobos, 2016; Crain et al, 2016)
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