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

Sara B Tirado Tolosa, Susan St Dennis, Tara Enders, Nathan M. Springer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Article numbere20013
JournalPlant Phenome Journal
Issue number1
StatePublished - 2021

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation Plant Genome Award IOS‐1444456. Sara B. Tirado was supported by the Bayer Graduate Student Fellowship. We thank Candice N. Hirsch, Cory D. Hirsch, and Zhikai Liang for providing feedback and insight about the work executed. We also thank Kjell Sandstrom, Shale Demuth, and Danielle Sorensen for helping collect RGB images and Amber Troesch for helping identify plant center coordinates.

Publisher Copyright:
© 2021 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America


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