In-Season Wheat Phenotyping for Breeding and Agronomy Applications.

The integration of remote sensing and ancillary data can provide quantitative and dynamic information on crop stress status and other important in-season characteristics. This paper discusses how hyperspectral data, digital images, IR canopy temperature and other data can be combined to fingerprint genotypic and environmental differences in wheat growing under different levels of water and N availability. Crop simulation results utilising some of these variables as inputs are discussed as a tool to predict the phenotype of complex traits, such as water use.