Digital phenotyping delivers diverse parameters that describe the phenotypic appearance of a plant. In combination with time-course data, which indicate how the parameters change over time, such datasets give a holistic view on how a plant performs.
The performance is not only the growth, but growth data are highly important to determine whether a plant does well in plant-environment interactions or with respect to its genomic background. Other phenomic parameters, such as morphology, colour (reflectance in visible wavelengths), fluorescence, or reflectance in non-visible wavelengths contribute similarly to the determination of performance as they are descriptors of stress responses, physiological properties, disease resistance, or genetic effects.
Growth, morphological parameters, and colour-indicated physiological factors (e.g. greenness, chlorosis, necrosis) are usually derived from RGB-images recorded in visible light. Physiological phenotyping frequently demands for data beyond the classical visible light images, with chlorophyll fluorescence data being a central component. PAM imaging of chlorophyll fluorescence is informative of photosystem II capacity and activity. Non-modulated fluorescence data indicate stress responses and senescence. A further key technology in rating physiological performance is hyper- and multispectral imaging, where spectrally resolved reflectance signatures give insight in metabolic constituents as well as in environmental responses. Primarily for addressing water-relations, NIR– and IR-imaging provide data on water content and surface temperatures.