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 ...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 fromhyperspectral reflectance data in maizewere evaluated on newsample 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.展开更多
基金supported by the Office of Science(BER),U.S.Department of Energy,grant no.DE-SC0020355 to J.C.S.and Y.G.the National Science Foundation under grant OIA-1557417 to Y.G.and J.C.S.and OIA-1826781 to J.C.Ssupport from the Nebraska Research Initiative.
文摘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 fromhyperspectral reflectance data in maizewere evaluated on newsample 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.
