Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat

Highly repeatable,nondestructive,and high-throughput measures of above-ground biomass(AGB)and crop growth rate(CGR)are important for wheat improvement programs.This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR:3D voxel index(3DVI)and 3D profile index(3DPI).Across three field experiments,contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture,several concurrent measurements of LiDAR and AGB were made from jointing to anthesis.Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant,ranging from 0.31(P<0:05)to 0.86(P<0:0001),providing confidence in the LiDAR indices as effective surrogates for AGB.The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB,particularly under water limitation.The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments.However,across all experiments,the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB,except for the 3DPI in the water-limited environment.In our experiments,the repeatability of either LiDAR index was consistently higher than that of AGB,both at discrete time points and when CGR was calculated.These findings provide promising support for the reliable use of ground-based LiDAR,as a surrogate measure of AGB and CGR,for screening germplasm in research and wheat breeding.

Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI,RGB,and LiDAR

Canopy ground cover(GC)is an important agronomic measure for evaluating crop establishment and early growth.This study evaluates the reliability of GC estimates,in the presence of varying light and dew on leaves,from three different ground-based sensors:(1)normalized difference vegetation index(NDVI)from the commercially available GreenSeeker®;(2)RGB images from a digital camera,where GC was determined as the portion of pixels from each image meeting a greenness criterion(i.e.,ðGreen−RedÞ/ðGreen+RedÞ>0);and(3)LiDAR using two separate approaches:(a)GC from LiDAR red reflectance(whereby red reflectance less than five was classified as vegetation)and(b)GC from LiDAR height(whereby height greater than 10 cm was classified as vegetation).Hourly measurements were made early in the season at two different growth stages(tillering and stem elongation),among wheat genotypes highly diverse for canopy characteristics.The active NDVI showed the least variation through time and was particularly stable,regardless of the available light or the presence of dew.In addition,between-sample-time Pearson correlations for NDVI were consistently high and significant(P<0:0001),ranging from 0.89 to 0.98.In comparison,GC from LiDAR and RGB showed greater variation across sampling times,and LiDAR red reflectance was strongly influenced by the presence of dew.Excluding times when the light was exceedingly low,correlations between GC from RGB and NDVI were consistently high(ranging from 0.79 to 0.92).The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.