Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

Abstract

Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky-Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.

Ability of Modified Spectral Reflectance Indices for Estimating Growth and Photosynthetic Efficiency of Wheat under Saline Field Conditions

“…Various changes in biophysical and biochemical characteristics of the canopy (such as leaf chlorophyll and other photosynthetic pigments contents, leaf tissue structure, dry matter accumulation, photosynthetic efficiency, and plant water status) have been found to be change in relation to osmotic and ionic components of salinity stress, and these cause noticeable variability in the spectral reflectance of the canopy in the three parts of the spectrum (VIS, NIR, and SWIR) domains [4,21,25,26]. Therefore, several SRIs that incorporate wavelengths related to the abovementioned main physiological characteristics of plants were modified to indirectly estimate the growth and photosynthetic efficiency of crops under various environmental conditions.…”

“…Previous studies have also reported that the spectral region of 420-470 nm, which is the blue region, is effective for characterizing the absorption features of photosynthetic pigments (such as chlorophyll-a and -b, αand βcarotenes, and lutein) that have been affected by salinity stress [16,44]. In addition, the blue (480-500 nm), green (520-580 nm), red (640-680 nm), red edge (720-770 nm), and NIR (812-868, 884-809, and 918-930 nm) regions, which are related to photosynthetic pigments absorption features, leaf structure, and water absorption bands in the NIR region, have also been found to be regions sensitive to salt stress in different crops [19,21,45]. Similarly, strong linear and non-linear relationships have been found between vegetation indices formulated from VIS and NIR bands and Pn and Gs in plants subjected to different environmental stresses under field conditions [9,11,12,22,46,47].…”

“…Previous studies have also reported that the high spectral reflectance in the blue region (compared to that in the red, red edge, and NIR spectral regions) contributes to the feasibility of using SRIs constructed from the wavelengths from this region to detect the effects of salinity stress on crop growth [17][18][19][20]. Lara et al [21] also reported that the spectral reflectance regions (500-600 nm and 710 nm) are associated with salt tolerance in lettuce plants, and Maimaitiyiming et al [22] found that SRIs constructed from wavelengths located between 400-720 nm of spectral regions are significantly correlated with Gs within a vineyard under different levels of water stress.…”

Hyperspectral assessment of plant responses to multi‐stress environments: Prospects for managing protected agrosystems

“…A similar approach has been used to predict pigment (chlorophyll, carotenoid, anthocyanin) content in lettuce based on VIS‐NIR spectroscopy (Neto et al, ), as well as using spectral indices (Gazula, Kleinhenz, Scheerens, & Ling, ; Lopes et al, ; Xue & Yang, ). Lara et al () used hyperspectral imaging to evaluate the effect of irrigation water salinity in lettuce proposing two models based on a principal component analysis of spectra and on a specifically developed vegetation index. Spectroscopy has also been used to distinguish lettuce from weeds (Slaughter, Giles, Fennimore, & Smith, ), and for lettuce postharvest classification (e.g., Mo et al, ; Moura et al, ).…”

A Probabilistic Hyperspectral Imagery Restoration Method

“…Hyperspectral imagery (HSI) is 3D data containing both spatial and spectral information, which is widely used for lots of remote sensing related applications [1][2][3][4][5][6][7]. However, HSI is unavoidably influenced by multiple kinds of factors-including noise, stripe corruption, etc.-during imaging and acquisition [8], which decrease the image quality.…”