# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 4 Discussion  
It is necessary to detect high-temperature stress in garlic to assess the damage caused by abnormally high temperatures resulting from global warming. Therefore, this study aimed to develop models (PLS, LS-SVM, DNN, RP-CNN) capable of detecting high-temperature stress in garlic and compare their accuracy with known vegetation index models. Table 2 indicates that vegetation index models exhibited lower performance with an accuracy below 70% compared to machine learning and deep learning models. This can be attributed to the smaller number of wavelengths used in the vegetation index models compared to the machine learning and deep learning models.  
Tables 2 and 3 demonstrate that the majority of the 14 th day models outperformed the 7 th day models. This suggests that the effect of high-temperature stress on garlic crops becomes more prominent over time. The higher accuracy of the LS-SVM model  
Figure 7: PLS-DA, LS-SVM, DNN, and RP CNN modeling images.  
compared to the PLS-DA model in Tables 3 and 4 can be attributed to the phenomenon of high-temperature stress in garlic crops and the non-linear reflectance values exhibited by garlic crops. Abiotic stress, such as high temperature stress, can induce complex and nonlinear changes in the spectral characteristics of plants. These changes manifest as intricate relationships and interactions between different wavelengths in the spectral data. Linear models, such as PLS-DA, assume a linear relationship between the independent variables (spectral data) and the dependent variable (abiotic stress classes), which may not capture the intricate nonlinear relationships present in the data. The LS-SVM model, on the other hand, is well-suited to capture and exploit the nonlinear relationships in the spectral data. It utilizes the kernel trick, which allows it to implicitly map the data into a high-dimensional feature space, where it can find a linear decision boundary that effectively separates the different abiotic stress classes. This transformation enables the LS-SVM model to handle the nonlinear nature of the spectral differences caused by abiotic stress. The LS-SVM model effectively captures the complex and nonlinear variations in the spectral data, allowing it to better discriminate between different classes of abiotic stress in garlic crops. The model's flexibility in finding nonlinear decision boundaries provides a significant advantage in accurately distinguishing between healthy garlic plants and those experiencing high temperature stress. As the high-temperature stress persisted for 7 and 14 days, the crops adapted to the high-temperature environment, resulting in nonlinear reflectivity for garlic crops in high-temperature environments compared to normal environments. However, the deep learning models, DNN and RP-CNN, exhibited lower accuracy compared to the LS-SVM model. This is likely because the DNN and RP-CNN models failed to optimize the model to capture the non-linear relationships among the three groups: the control group, heat level 1, and heat level 2, unlike the LS-SVM model.  
Figure 7 presents the optimal images of garlic crops on the 14 th day, obtained by applying the model developed using the control group and heat level 2 data. Unlike the high discrimination accuracy observed when analyzing the spectra of each group, the applied model's images did not reveal clear differences between the groups in all areas and across all models. There was no significant distinction between the control group and the first stage of high temperature in the images, but noticeable differences were observed between the control group and the second stage of high temperature. In particular, the LS-SVM image demonstrated the most pronounced differences among the control group, heat level 1, and heat level 2. This aligns with the accuracy results obtained from the spectral-based models, indicating relatively better performance for the LS-SVM model.