Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings

Abstract

Abstract There is significant enthusiasm about the potential for hyperspectral imaging to document variation among plant species, genotypes, or growing conditions. However, in many cases the application of hyperspectral imaging is performed in highly controlled situations that focus on a flat portion of a leaf or side‐views of plants that would be difficult to obtain in field settings. We were interested in assessing the potential for applying hyperspectral imaging from a top‐down view to document variation in genotypes and abiotic stresses for maize (Zea mays L.) seedlings grown in controlled environments. A top‐down image of a maize seedling includes a view into the funnel‐like whorl at the center of the plant with several leaves radiating outward. There is substantial variability in the reflectance profile of different portions of this plant. To deal with the variability in reflectance profiles that arises from this morphology we implemented a method that divides the longest leaf into 10 segments of equal length from the center to the leaf tip. We show that there is large variability in the hyperspectral profiles across leaf segments, which are masked when performing whole‐plant averages as tend to be done when analyzing hyperspectral data. We found that using these segments provides improved ability to discriminate different genotypes (B73, Mo17, Ki11, MS71, PH207) and abiotic stress conditions (heat, cold, or salinity stress) for maize seedlings. This provides an approach that can be implemented to help classify genotype and environmental variation for maize seedlings from a top‐down view such as that which would be collected in field settings.