Figure 1.

Prediction of the genetic merit for grain yield using hyper-spectral crop image data. (A) Data consists of hyper-spectral reflectance data (${\mathit{x}}_i$) and phenotypic measurements of the target trait ($y_i$, e.g., grain yield). (B) A subset of the data (the training set) is used to derive the coefficients ($\mathit{\beta }$) of a selection index. (C) These coefficients are then applied to image data of individuals in the testing set to derive the index ($I_i$) for each individual. The predictive ability of the index is assessed by calculating the accuracy of indirect selection ($Acc\left(I\right)$) in the testing set.