article · Frontiers in Plant Science
This research evaluates the use of non-destructive proximal spectral reflectance data to assess key agronomic parameters in spring wheat lines under full and limited irrigation. Working with 30 recombinant inbred lines across two generations, the study evaluated aboveground dry weight, biomass water content, and grain yield. Researchers compared the accuracy of diverse spectral reflectance indices alongside partial least squares regression and stepwise multiple linear regression models. Overall, estimations proved more accurate under limited irrigation and combined conditions than under full irrigation. Models combining specific wavelengths across visible, red edge, and near-infrared ranges captured significant genetic variation, explaining up to 81 percent of the variation in grain yield under drought stress. The findings demonstrate that optical sensing can provide reliable, high-heritability screening criteria to accelerate crop breeding for arid environments.
Water scarcity poses a major threat to agricultural productivity in arid regions. Breeding drought-resilient crops typically requires slow, costly, and destructive field sampling. Demonstrating that optical sensors and mathematical models can accurately measure wheat biomass, water content, and yield non-destructively helps plant breeders rapidly evaluate large numbers of candidate varieties, speeding up the development of climate-resilient crops.
This work is an applied study targeting wheat breeders and agricultural research organisations looking to replace destructive field evaluations with rapid optical screening. By defining specific wavelengths and models to assess drought performance, it enables integration into proximal sensor platforms or phenotyping software. The research represents an applied, tested methodology that requires further integration and validation before direct commercial adoption in field breeding programmes.
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The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.
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DOI: 10.3389/fpls.2019.01537
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