*sgraham12@huskers.unl.edu, kfrels2@unl.edu

The expanding region of the Wheat Stem Sawfly (WSS) threatens wheat in the Great Plains region of the United States. Although increased stem solidness improves resistance to WSS, developing solid-stemmed cultivars requires time-consuming and destructive phenotyping methods.

To expedite development of WSS resistant cultivars a high-throughput phenotyping method for evaluating stem solidness and WSS infestation is needed. Therefore, we aimed to assess the potential of phenomic prediction with uncrewed aerial systems (UAS) to predict stem solidness, WSS infestation, and yield in wheat.

Multispectral and red-green-blue UAS data was collected at several time points at two naturally infested locations in Western Nebraska from 2022-2023. The UAS measurements were used to calculate spectral reflectance indices, which were compared with yield, plant height, stem solidness, and WSS infestation. Linear and ridge regression models were then trained to use spectral indices to predict yield and WSS infestation. We found plant height and stem solidness were significantly negatively correlated (R2 = -0.36) and stem solidness did not affect yield (R2 = 0.02).

At flowering, WSS infestation was significantly correlated to the green band (R2 = 0.36), and during grain fill, stem solidness was significantly correlated with several indices. For WSS, the prediction accuracies were -0.44 for ridge regression and 0.06 for linear regression. Despite the significant correlations, our ability to predict WSS resistance was low, and we did not find a viable high-throughput phenotyping system for WSS.

Plant breeders will continue progressing with labor-intensive WSS screening methods while searching for an improved phenotyping system.