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Utilizing top-down hyperspectral imaging for monitoring genotype and growth conditions in maize

View ORCID ProfileSara B. Tirado, Susan St Dennis, View ORCID ProfileTara A. Enders, View ORCID ProfileNathan M. Springer
doi: https://doi.org/10.1101/2020.01.21.914069
Sara B. Tirado
1Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108
2Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
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Susan St Dennis
2Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
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Tara A. Enders
3Department of Biology, Hofstra University, Hempstead, NY 11549
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  • For correspondence: springer@umn.edu tara.a.enders@hofstra.edu
Nathan M. Springer
2Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
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  • For correspondence: springer@umn.edu tara.a.enders@hofstra.edu
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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 to document variation in genotypes or abiotic stresses in a fashion that could be implemented in field settings. Specifically, we focused on collecting top-down hyperspectral images of maize seedlings similar to a view that would be collected in a typical maize field. 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 outwards. 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 from the center to the leaf tip. We show that using these segments provides improved ability to discriminate different genotypes or abiotic stress conditions (heat, cold or salinity stress) for maize seedlings. We also found substantial differences in the ability to successfully classify abiotic stress conditions among different inbred genotypes of maize. This provides an approach that can be implemented to help classify genotype and environmental variation for maize seedlings that could be implemented in field settings.

Significance Statement This study describes the importance of using spatial information for the analysis of hyperspectral images of maize seedling. The segmentation of maize seedling leaves provides improved resolution for using hyperspectral variation to document genotypic and environmental variation in maize.

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  • Conflict of interest: The authors do not have any conflict of interest to declare.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 23, 2020.
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Utilizing top-down hyperspectral imaging for monitoring genotype and growth conditions in maize
Sara B. Tirado, Susan St Dennis, Tara A. Enders, Nathan M. Springer
bioRxiv 2020.01.21.914069; doi: https://doi.org/10.1101/2020.01.21.914069
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Utilizing top-down hyperspectral imaging for monitoring genotype and growth conditions in maize
Sara B. Tirado, Susan St Dennis, Tara A. Enders, Nathan M. Springer
bioRxiv 2020.01.21.914069; doi: https://doi.org/10.1101/2020.01.21.914069

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