# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 3 Results
### PLSR Model Performance  
The prediction model was developed based on multiple preprocessing techniques, and the best-performing preprocessing model was used for further analysis, as shown in Table 2. Among the various preprocessing methods, the raw spectra-based model exhibited the best performance. As a result, the scatter plot in Figure 10a,b shows a strong correlation between the actual measured chlorophyll quantity in the plants and the predicted amount of chlorophyll in the meter readings. As shown in Table 2, the raw preprocessing presented high accuracy among all preprocessing methods, with lower errors. The calibration correlation between the spectra and the chemical content of plants was high, with an \(\text{R}^{2}_{\text{calib}}\) value of 0.91. At the same time, the model validation had an \(\text{R}^{2}_{\text{cal}}\) of 0.90 based on raw preprocessing, with a RMSEV of 3.59%. Based on the comparison in Table 2, the determination coefficient of calibration and prediction was over 0.91 for all developed models. The plots in Figure 10a,b show the calibration and prediction results, respectively. The y-axis corresponds to the measured plant's greenness composition value based on SPAD-502, and the x-axis represents the predicted SPAD-measured composition as a linear correlation of calibration and prediction. According to the plotted results in Figure 10, the red color indicates lower holding SPAD values compared to the resistant plants. The calibration and validation data plot distributions follow the consistency of Figure 6a,b spectral means and generated ratio images in Figure 9.  
Furthermore, this study used the beta coefficient of the PLSR model to estimate the chemical distribution of greenness contents in both the control and stressed ginseng plants. Figure 11 shows the beta coefficient, where the peaks at around 680 nm to 745 nm represent chlorophyll spectral peaks. Different analyses reported the composition corresponding to \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Preprocessing Methods**} & \multicolumn{5}{c}{**Calibration and Validation Results**} \\ \cline{2-6}  & \(\text{R}^{2}_{\text{calib}}\) & **SEC (\%)** & \(\text{R}^{2}_{\text{cal}}\) & **RMSE (\%)** & **LV** \\ \hline Raw & 0.91 & 2.85 & 0.90 & 3.59 & 10 \\ Smoothing & 0.89 & 2.89 & 0.89 & 3.61 & 10 \\ Max normalization & 0.91 & 2.87 & 0.89 & 3.61 & 10 \\ Mean normalization & 0.90 & 2.96 & 0.89 & 3.77 & 10 \\ Range normalization & 0.91 & 2.87 & 0.89 & 3.61 & 10 \\ MSC & 0.91 & 2.87 & 0.89 & 3.65 & 10 \\ SNV & 0.91 & 2.87 & 0.89 & 3.65 & 10 \\ SG 1 st derivatives & 0.89 & 3.30 & 0.89 & 3.74 & 10 \\ SG 2 nd derivatives & 0.90 & 3.01 & 0.89 & 3.74 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PLSR results were obtained to predict the ginseng plants’ greenness composition. Figure 10: (**a**) Calibration (**b**) validation plots of the established PLSR model. exact bands or closely around the most visible bands, and most vitally in related peaks observed in our study, circled in Figure 11.  
The chemical mapping of the ginseng plants presented in Figures 12 and 13 shows the spatial distribution of the greenness level on the surfaces of leaves and has a high correlation to chlorophyll concentration, which is essential for determining pigment composition in plants. Therefore, the potential concentration of greenness in plants was determined via chemical mapping, in which the color scale from blue to red in the y-axis of Figures 12 and 13 represents the chemical concentration, or the plants with more greenness, consisting of more chlorophyll concentration. Additionally, the intensity representation of each group of plants was plotted on a histogram to better visualize changes in the plants. In the histogram plots, the x-axis indicates the responses to the intensity of pixels, and the y-axis corresponds to the number of pixels at each intensity value presented by the image.