# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 3 Results and Discussion
### Validation of the Models to Evaluate Salinity Effects  
In order to test the performance of the prediction models generated on the calibration set, both models (PC1 on SG D2 SNV spectra and LSI index) were applied to the hyperspectral images of the leaves of the validation set, thus obtaining corresponding virtual images for each case of the two sets. Table 4 presents the results of the one-way ANOVA's and Tukey-Kramer tests performed on the LSI indexes and PCA scores of \(n=1000\) spectra from each leaf of the validation set, considering as grouping variable the salinity treatment. Differences between the leaves corresponding to the Ct treatment and the S3 treatment (Figure 8) were observed with both models. It can be seen that the virtual images of both models for the same leaf showed a similar distribution of pixel values. Both types of virtual images were able to sense the effect of salinity in a similar way. However, the LSI-based model was simpler and faster to apply because it is based solely on the reflectance at three different wavelengths.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **F-Fisher** & \(p\)**-Value** & & **Mean** & **Standard Error** \\ \hline \multirow{4}{*}{LSI} & \multirow{4}{*}{1491.57} & \multirow{4}{*}{0} & \multicolumn{2}{c}{Ct} & 0.68830a & 0.00102 \\  & & & S1 & 0.65912b & 0.00102 \\  & & & S2 & 0.63788c & 0.00102 \\  & & & S3 & 0.62661d & 0.00102 \\ \hline \multirow{4}{*}{Scores PC1} & \multirow{4}{*}{1197.02} & \multirow{4}{*}{0} & \multicolumn{2}{c}{Ct} & \(10.53830\)a & 0.04345 \\  & & & S1 & \(9.64646\)b & 0.04345 \\ \cline{1-1}  & & & S2 & \(9.46616\)c & 0.04345 \\ \cline{1-1}  & & & S3 & \(10.04068d\)d & 0.04345 \\ \hline \multirow{4}{*}{GIR} & \multirow{4}{*}{1340.2} & \multirow{4}{*}{0} & \multicolumn{2}{c}{Ct} & 0.35211a & 0.00100 \\  & & & S1 & \(0.38783\)b & 0.00100 \\ \cline{1-1}  & & & S2 & \(0.41381\)c & 0.00100 \\ \cline{1-1}  & & & S3 & \(0.43755\)d & 0.00100 \\ \hline \multirow{4}{*}{Scores PC1} & \multirow{4}{*}{2845.88} & \multirow{4}{*}{0} & \multicolumn{2}{c}{Ct} & \(-1.0902\)a & 0.000227 \\ \cline{1-1}  & & & S1 & \(-0.6474\)b & 0.000228 \\ \cline{1-1} \cline{2-5}  
It can be observed in the images that the pixels from the external parts of the leaves are the most affected by the salinity. The salinity effect is more intense in the areas furthest away from the veins of the leaves. In an intact plant, water is transported to the leaves through the xylem of vascular bundles that branch into a fine network of nerves through the leaf [48]. Salinity restricts water uptake and transpiration and thus reduces water and nutrient uptake and transport to young leaves [49]. This limitation in the xylem transport due to the effect of salinity could explain why the most affected areas of the leaves correspond to areas with thin venation (leaf margin).  
As it was introduced, salinity stress can induce marginal necrosis and tip burn in the leaves of lettuce. These symptoms have recently been associated with local oxidative stress in leaf margins that appear before symptom expression [9]. This pattern is coincident with the distribution of the most  
Figure 8: Virtual images obtained by applying both models developed on the hyperspectral images of lettuce leaves, from the validation set. Pixels colored from blue to red are showing increasing levels of salinity affectation. Left: PC1 model. Right: LSI model.  
affected pixels by salinity in our images. Because of this, hyperspectral images could be used to detect symptoms of salt stress in leaves before visible damage expression and thus avoid significant economic losses in crop production.