Increasing the tolerance of maize seedlings to low‐temperature episodes could mitigate the effects of increasing climate variability on yield. To aid progress toward this goal, we established a growth chamber‐based system for subjecting seedlings of 40 maize inbred genotypes to a defined, temporary cold stress while collecting digital profile images over a 9‐daytime course. Image analysis performed with Plant CV software quantified shoot height, shoot area, 14 other morphological traits, and necrosis identified by color analysis. Hierarchical clustering of changes in growth rates of morphological traits and quantification of leaf necrosis over two time intervals resulted in three clusters of genotypes, which are characterized by unique responses to cold stress. For any given genotype, the set of traits with similar growth rates is unique. However, the patterns among traits are different between genotypes. Cold sensitivity was not correlated with the latitude where the inbred varieties were released suggesting potential further improvement for this trait. This work will serve as the basis for future experiments investigating the genetic basis of recovery to cold stress in maize seedlings.

16Increasing the tolerance of maize seedlings to low temperature episodes could mitigate the 17 effects of increasing climate variability on yield. To aid progress toward this goal, we established 18 a growth chamber-based system for subjecting seedlings of 40 maize inbred genotypes to a 19 defined, temporary cold stress while collecting digital profile images over a 9-day time course. 20Image analysis performed with PlantCV software quantified shoot height, shoot area, 14 other 21 morphological traits, and necrosis identified by color analysis. Hierarchical clustering of changes 22in growth rates of morphological traits and quantification of leaf necrosis over two time intervals 23 resulted in three clusters of genotypes, which are characterized by unique responses to cold 24 stress. For any given genotype, the set of traits with similar growth rates is unique. However, the 25 patterns among traits are different between genotypes. Cold sensitivity was not correlated with 26 the latitude where the inbred varieties were released suggesting potential further improvement 27 for this trait. This work will serve as the basis for future experiments investigating the genetic 28 basis of recovery to cold stress in maize seedlings. 29

Author contributions: SBT, TAE and NMS conceived the experiments; SBT, TAE and SSD conducted the experiments, SBT and TAE conducted the analyses; SBT and NMS wrote the manuscript Keywords: hyperspectral imaging, plant phenotyping, abiotic stress, maize Conflict of interest: The authors do not have any conflict of interest to declare. Abstract 1There is significant enthusiasm about the potential for hyperspectral imaging to document 2 variation among plant species, genotypes or growing conditions. However, in many cases the 3 application of hyperspectral imaging is performed in highly controlled situations that focus on a 4 flat portion of a leaf or side-views of plants that would be difficult to obtain in field settings. We 5 were interested in assessing the potential for applying hyperspectral imaging to document 6 variation in genotypes or abiotic stresses in a fashion that could be implemented in field settings. 7Specifically, we focused on collecting top-down hyperspectral images of maize seedlings similar 8 to a view that would be collected in a typical maize field. A top-down image of a maize seedling 9 includes a view into the funnel-like whorl at the center of the plant with several leaves radiating 10 outwards. There is substantial variability in the reflectance profile of different portions of this 11 plant. To deal with the variability in reflectance profiles that arises from this morphology we 12 implemented a method that divides the longest leaf into 10 segments from the center to the leaf 13 tip. We show that using these segments provides improved ability to discriminate different 14 genotypes or abiotic stress conditions (heat, cold or salinity stress) for maize seedlings. We also 15 found substantial differences in the ability to successfully classify abiotic stress conditions 16 among different inbred genotypes of maize. This provides an approach that can be implemented 17 to help classify genotype and environmental variation for maize seedlings that could be 18 implemented in field settings. 19 Significance Statement 20This study describes the importance of using spatial information for the analysis of hyperspectral 21 images of maize seedling. The segmentation of maize seedling leaves provides improved containing plant tissue and thresholded to generate a binary image mask to extract the reflectance 129 values at each wavelength for entire plants. 130This approach was applied to several different experiments that are summarized in Table S1. We 131 sought to address different themes in our analyses of this data. First, different genotypes or 132 environments often result in changes in plant morphology (Enders et al., 2019). We evaluated the 133 potential of hyperspectral data to capture morphological differences by utilizing changes in

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. 1 INTRODUCTION Abiotic stresses cause major yield declines across many crops and can limit production by up to 70% (Boyer, 1982; Majeed & Muhammad, 2019). Advances in molecular tools have greatly facilitated breeders in efficiently identifying and Abbreviations: DAS, days after sowing; PCA, principal component analysis; RGB, red-green-blue; SVM, support vector machine; ZT, Zeitgeber Time. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.