Leaf Spectral Reflectance of Maize Seedlings and Its Relationship to Cold Tolerance

Abstract

Increasing early‐season cold tolerance of maize (Zea mays L.) has the potential to lengthen its growing season, reduce its environmental impact, and enhance its yields. Cold‐ and warm‐grown plants differ for biomass accumulation and spectral reflectance, the latter caused by differences in leaf chlorophyll content, carotenoid content, or other chemical and morphological attributes. Here, we evaluate genetic leaf spectral reflectance diversity across 38 inbred and 14 hybrid maize genotypes grown in cold and control temperatures. Genotypes varied for spectral reflectance indices correlated with chlorophyll content and for an index correlated with the ratio of leaf carotenoids to chlorophylls. Genotypic differences greatly contributed to spectral reflectance variation across all visible wavelengths, with the greatest genetic variation between 500 and 600 nm and around 700 nm. Cold treatment effects were most significant across the same wavelengths. Spectral indices indicated lower chlorophyll and a higher carotenoid/chlorophyll level in cold‐exposed plants relative to control plants. Genotype × temperature interactions were small relative to genotype effects. Cold tolerance, measured as the ratio of dry matter accumulation in cold‐treated plants relative to control plants, varied among hybrids and inbreds. Correlations of cold tolerance with known spectral reflectance indices, reflectance first derivatives, and novel, normalized spectral indices were mostly low and occurred only in certain germplasm. One reflectance parameter, the reflectance curve slope at 828 nm, was consistently related to genotypic cold tolerance, even when measured on plants that had not been exposed to cold.

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

“…However, identifying more subtle symptoms such as those caused by abiotic stresses can be challenging. Pandey et al (2017) found hyperspectral imaging to be useful in quantifying plant leaf chemical properties that could aid in detecting water and nutrient deficiencies among crops and Obeidat et al (2018) discovered that spectral indices correlated with chlorophyll content could help distinguish between genotypes and cold-stressed plants in indoor settings. Similarly, Behmann et al (2014) found that hyperspectral imaging can be used to cluster barley plant pixels into different levels of drought-stress based on amount of chlorosis and senescence.…”

“…Fewer studies have used hyperspectral profiling to separate different genotypes or lines of the same species. A study by Obeidat et al (2018) showed that genotype main effects across short-season maize lines significantly contributed to variation in various spectral reflectance indices and spectral reflectance in the visible and near-infrared range when comparing hyperspectral scans of flat leaves. This variation, particularly in the spectral reflectance across the visible range of the spectrum, was likely due to chlorophyll and carotenoid differences (Obeidat et al, 2018).…”

“…A study by Obeidat et al (2018) showed that genotype main effects across short-season maize lines significantly contributed to variation in various spectral reflectance indices and spectral reflectance in the visible and near-infrared range when comparing hyperspectral scans of flat leaves. This variation, particularly in the spectral reflectance across the visible range of the spectrum, was likely due to chlorophyll and carotenoid differences (Obeidat et al, 2018). To assess variation in spectral reflectance among maize genotypes, we applied our leaf segmentation approach to compare the reflectance values across individual leaf segments among the different inbred lines grown in control conditions in experiment E1.…”

Estimating Productivity, Detecting Biotic Disturbances, and Assessing the Health State of Traditional Olive Groves, Using Nondestructive Phenotypic Techniques

“…Along with IRT, plant phenotyping spectral reflectance indices related to plant photosynthetic status such as leaf and crown chlorophyll concentration [57][58][59][60], obtained in situ and noninvasively, can reflect the health state of plants. After all, chlorophyll as a pivotal photosynthetic pigment on which plant growth and productivity depend [60,61], is considered a hallmark index to plant health estimation [62][63][64].…”

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

“…A 206 study by Obeidat et al (2018) showed that genotype main effects across short-season maize lines 207 significantly contributed to variation in various spectral reflectance indices as well as spectral 208 reflectance in the visible and near-infrared range when comparing hyperspectral scans of flat 209leaves. This variation, particularly in the spectral reflectance across the visible range of the 210 spectrum, was likely due to chlorophyll and carotenoid differences(Obeidat et al, 2018). To 211 assess variation in spectral reflectance among maize genotypes, we applied our leaf segmentation 212 approach to compare the reflectance values across individual leaf segments among the different 213 inbred lines grown in control conditions in experiment E1.…”