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.
Highlights
A growing number of studies have demonstrated the use of hyperspectral reflectance data from spectrometers to build models that can predict a range of plant traits (Table 1)
Hyperspectral reflectance-based phenotyping for quantitative genetics spectral regions that are well-known absorption peaks maxima for important plant pigments: chlorophyll and carotenoids
Hyperspectral data can be collected from satellites, from unmanned aerial vehicles (UAVs) or planes, from cameras on the ground, or from handheld spectrometers in direct contact with the plant surface
Summary
A growing number of studies have demonstrated the use of hyperspectral reflectance data from spectrometers to build models that can predict a range of plant traits (Table 1). In a study employing data from a maize association panel grown in three environments, Ge et al (2019) demonstrated the ability to predict leaf nitrogen, phosphorus, and potassium content with good accuracy from hyperspectral reflectance data.
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