Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Abstract

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Keywords: hyperspectral reflectance; maize; phenotyping; quantitative genetics.

Figure 1

Collection of hyperspectral reflectance data in a maize genetics experiment. (A) Use of a portable and battery-powered spectroradiometer to collect hyperspectral reflectance data as part of a maize field experiment conducted in the summer of 2020. (B) Variation in patterns of hyperspectral reflectance observed among the leaves of four distinct maize inbred genotypes. Each datapoint consisted of the measurements of 2151 distinct spectral intensities between 350 and 2500 nm in wavelength.

Figure 2

Evaluation of model performance built from data from 2018 (Ge et al., 2019) on data from 2019. Upper left R² values show coefficient of determination for presented data, whereas bottom right R² values are obtained by cross-validation on 2018 data (Ge et al., 2019). CHL, chlorophyll content; LWC, leaf water content; SLA, specific leaf area; N, nitrogen content; K, potassium content; P, phosphorus content.

Figure 3

Comparison between narrow-sense heritability for ground truth and predicted from spectra values. Dashed lines indicate expected narrow-sense heritability value obtain by multiplying ground truth narrow-sense heritability value with R² values from model performance evaluation. CHL, chlorophyll content; N, nitrogen content; SLA, specific leaf area; HN, high nitrogen condition; LN, low nitrogen condition.