Scientific Publication

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data

author(s) Montesinos-Lopez, O.A - Montesinos-Lopez, A - Crossa, J - De los Campos, G - Alvarado Beltrán, G - Mondal, S - Rutkoski, J - Gonzalez-Perez, L - Burgueño, J
from International Maize and Wheat Improvement Center (CIMMYT)
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themes
regions
  • Western Africa
  • Sub-Saharan Africa
  • Africa
  • World
citation

Montesinos-Lopez, O.A et al., 'Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data', Plant Methods, vol. 13(1):4, Springer Science and Business Media LLC, 2017

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Abstract

Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT’s global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy

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