Identification of Metal Stresses in Arabidopsis thaliana Using Hyperspectral Reflectance Imaging

Abstract

Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana: salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

“…Hyperspectral imaging detects mineral nutrients indirectly in the visible and near-infrared regions by detecting these organic complexes [63][64][65][66]. Reflectance in the visible region (400-700 nm) may be associated with pigment molecules such as chlorophyll, and reflectance in the near-infrared region (700-1000 nm) may be associated with protein, fatty acid and starch molecules [67][68][69]. The most important wavelengths for predicting the N concentration were found in the 400-500 nm, 520-680 nm, 700-820 nm and 850-1000 nm regions.…”

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

“…However, it should be noted that LKC in cotton is not highly correlated with wavelet coefficients in the whole band of 400–950 nm but only in some important spectral regions ( Figure 5 ). Therefore, to reduce collinearity between spectral data dimensions and adjacent wavelengths, screening several effective wavelengths containing maximum spectral information plays an important role in reducing model complexity and improving estimation ability (Lu et al, 2019b ; Ruffing et al, 2021 ). In this study, characteristic wavelengths of the R spectrum and CWT spectra at three growth periods were screened based on the CARS and RF algorithms ( Table 3 ).…”

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

“…IR imaging primarily refers to reflected-IR imaging, which covers the NIR and SWIR regions and is similar to multispectral or hyperspectral imaging. This imaging can measure various biochemicals such as water content, mesophyll cell structure, nitrogen protein, and cellulose [26,71,75]. IR imaging is often combined with RGB imaging to detect physiological status and screen morphological traits under stress.…”

“…Therefore, 2D imaging is now the most widely used form of plant stress phenotyping [22][23][24]. Two-dimensional imaging comprises visible, multispectral, hyperspectral, IR, thermal-IR, and ChlF imaging [22,25,26]. Similarly, 2D imaging can be divided into S-2D (visible, multispectral, hyperspectral, and IR imaging) and T-2D (ChlF imaging) in general conditions.…”