# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 4 Discussion
### PLSR Results Analysis  
Among different preprocessing approaches, raw preprocessing demonstrated a higher accuracy, as plotted in Figure 10, with compatibility in the observed changes, respectively, in the mean spectral data in Figure 6 and the demonstrated mean SPAD values in Figure 7. Furthermore, the obtained beta coefficient was used to develop a chemical visualization of the control and stressed plants. The observed peaks in the beta coefficient at 683 nm and 731 nm, previously discussed in the spectral interpretation of data, correlated to chlorophyll bands. According to previous reports, the peaks at around 683 nm [71] and 688 nm are related to reflectance bands of chlorophyll emission [36; 67]. Furthermore, the peaks at around 683 nm and 731 nm [66], peaks around 698 nm [72], are reported as the chlorophyll bands, and there are other bands around the region of 726 nm and 745 nm corresponding to chlorophyll emissions [30; 47]. Thus, this study demonstrates the potential of hyperspectral fluorescence imaging for predicting plant-relative chlorophyll compositions in plant leaves. Hyperspectral imaging has significant potential for the early visualization of chemical composition changes in abiotically stressed plants, including disease detection in tobacco plants [18]. For example, other research has reported the image-based visualization of changes in chemical properties, detection of the severity as well as the quantification of Pseudomonas Cichorii in tomato plants (using hyperspectral fluorescence imaging) [31], the determination of nitrogen distribution in pepper plants [73], the detection of freezing stress in wheat plants [74], and prediction of nicotine content in tobacco [75]. For this purpose, the corrected hyperspectral image was processed to visualize the correlation between greenness and chemical composition changes in ginseng plants. In contrast, the resistant groups of plants were less susceptible or more resistant than the sensitive groups of plants. Furthermore, the chemical images showed that the sensitive groups of plants were more vulnerable to high temperatures than the resistant group (see Figures 13a-f). Additionally, the same pattern was observed in the histogram representations, in which the stressed plants from resistant varieties showed the most significant number of pixels corresponding to the highest intensity peaks (over 2500 pixels; see Figure 13c,f). At the same time, the most significant number of pixels in the stressed plants corresponded to sensitive groups (1000 to 1700 pixels; see Figure 12c,f). In this way, the present study confirmed the resistance of Suni and Sunmyoung for two sensitive groups, Chunpoong and Jakeyong. However, this study did not correlate the chlorophyll concentrations with spectral features. Instead, the SPAD readings presented a mapping of greenness levels in plants, with the general theory that plants with higher greenness levels will consist of higher chlorophyll concentrations. Despite significant advantages, the hyperspectral fluorescence measurements require the adaptation of the plants to the dark and limit their application for outdoor phenotyping. Despite breakthroughs in fluorescence imaging technologies, SIF transcends the dark adaptation phase and can be used as an alternative. These approaches may be applicable in different scales and present considerable potential, though they do not visualize the absolute photosynthetic biochemistry of plant data. However, these approaches still require considerable modification [76]. Therefore, more research and novel non-destructive real-time operating systems are required to evaluate plant status. Furthermore, farmers and investors are more interested in low-cost devices for detecting unhealthy crops.