## 3 Results
### Crop Health Assessment  
The correlation of the vegetation indices with chlorophyll content was calculated for all samples, including both healthy and unhealthy crops, with a significance threshold of \(p<0.05\), using [PERSON]'s correlation coefficient. [PERSON]'s correlation matrix helped to identify the most significant correlations (Figure 6). The SR, GNDVI, and MCARI indices showed a positive correlation with chlorophyll content (\(r\) = 0.88, 0.69, and 0.60, respectively). ARI and SIPI also exhibited a stronger correlation (\(r\) = 0.52 and 0.55, respectively). A low correlation was observed for NDVI (\(r\) = 0.10) and CLr (\(r\) = 0.18). Furthermore, among the vegetation indices analyzed, only NDVI did not reach the significance threshold (\(p\)-value > 0.05). Vegetation indices have been widely used to quantify plant photosynthetic absorption, particularly chlorophyll content and anthocyanins. The SR index serves as an indicator of canopy structure, light absorption, and photosynthetic capacity [19], while GNDVI and MCARI are more sensitive to foliar chlorophyll and have been used to estimate photosynthetic activity under water stress conditions [27]. On the other hand, NARI and mARI showed a strong negative correlation with chlorophyll content (\(r\) = \(-\)0.86 and \(-\)0.66, respectively). These VIs were highly correlated with anthocyanin content [46], which was not considered in this study. Although the correlation results were not optimal for a predictive model of plant photochemical content under water deficit conditions, baby lettuce exhibited reduced leaf area and higher chlorophyll values. The significance of the differences between leaf area and chlorophyll values for healthy and non-irrigated plants was analyzed using a post hoc Tukey test (\(\alpha\) = 0.05). As shown in the statistics in Figure 7, this significant difference may reflect a plant strategy to optimize photosynthetic efficiency under water stress, which helps explain the observed correlations. As a reaction to water stress, crops typically show foliar adaptation mechanisms to reduce light absorption, such as decreased chlorophyll concentration and down-regulation of photosynthesis [47], as well as increased concentrations of antioxidant components [48]. In addition, wilting and reduced leaf area in water-stressed crops are well documented [49]  
Figure 6: Correlation matrix plot of Pearson’s coefficient among chlorophyll content and different vegetation indices for the lettuce leaves. and have a significant influence on biomass and yield [50]. However, the effect of water stress is strongly influenced by the time scale and phenological stage of the plant cycle [48]. In this study, differences were observed only at harvest time, which limits the possibility of conducting a more in-depth analysis of the physiological state of the plant at different stress conditions. Moreover, the main goal of testing the application of the MS image alignment model was achieved with satisfactory results. Figure 8 demonstrates how the accurate alignment of the MS bands using the CB method effectively distinguished specific leaf areas through the vegetation indices. This provides a promising foundation for future sub-pixel analyses of leaf areas. Although the correlations were not strong enough to propose a predictive model for crop physiological status, the distribution of experimental data from several VIs, such as SR, NARI, GNDVI, and MCARI, clearly differentiated between healthy and unhealthy plants. This suggested that properly aligned band ratios could effectively distinguish between these two physiological conditions. Figure 8: Selected vegetation indices (VIs) (GNDVI, SR, MCARI, NARI, and mARI) were applied to baby lettuce leaves as a result of image alignment on healthy and stressed plants.