# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 3 Results  
### Model Results  
Table 2 presents the results of the vegetation index model on the 7 th and 14 th days. On the 7 th day, with the exception of the PRI results in the control group and Heat level 1, the accuracy of NDVI, Red Edge Ratio, and PRI models was similar. However, the accuracy of all three models remained below 70%. On the 14 th day, the Red Edge Ratio model exhibited higher accuracy compared to the other models in heat level 1 and heat level 2. Nevertheless, the accuracy of all three models remained below 70%. Table 3 presents the results of the PLS-DA, LS-SVM, DNN, and RP-CNN models created using three groups and two groups on the 7 th day of the garlic bulb enlargement period. When examining the accuracy of the models in the control group and heat level 1, it was found that among the three-group models, the LS-SVM model achieved the highest accuracy, while the other models showed no significant difference. Among the two-group models, the LS-SVM model also had the highest accuracy, with no significant difference observed among the remaining models. The PLS-DA, DNN, and RP-CNN models exhibited improved accuracy in the two-group models compared to the three-group models. Analyzing the accuracy of the models in the control group and heat level 2, the LS-SVM model displayed the highest accuracy among the three-group models, followed by the PLS-DA, DNN, and RP-CNN models. Among the two-group models, the LS-SVM model achieved the highest accuracy, followed by the PLS-DA model. The accuracy of the DNN model did not significantly differ from that of the RP-CNN model. The RP-CNN model demonstrated improved accuracy in the two-group model compared to the three-group model. When examining the accuracy of the models in heat levels 1 and 2, it was found that the LS-SVM model was overfitted, while the RP-CNN model achieved the highest accuracy. The accuracy of the PLS-DA and DNN models did not significantly differ. Among the two-group models, the LS-SVM models had the highest accuracy, while the PLS-DA, DNN, and RP-CNN models showed no significant difference in accuracy. The LS-SVM model's overfitting was resolved in the two-group model, and the PLS-DA, DNN, and RP-CNN models demonstrated improved accuracy compared to the three-group model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Accuracy (\%)** & \\ \cline{3-5}  & & **Control Group** & **Control Group** & **Heat Level 1** \\  & **vs. Heat Level 1** & **vs. Heat Level 2** & **vs. Heat Level 2** \\ \hline \multirow{3}{*}{7 th day} & NDVI & 63.06 & 55.69 & 57.06 \\  & Red Edge & 60.38 & 56.38 & 52.63 \\  & PRI & 47.94 & 50.13 & 52.38 \\ \hline \multirow{3}{*}{14 th day} & NDVI & 56.56 & 54.94 & 49.75 \\  & Red Edge & 60.56 & 54.19 & 64 \\ \cline{1-1}  & PRI & 52.88 & 55.31 & 54.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of all models on the 7 th day the of garlic bulb enlargement period. Table 4 presents the results of the PLS-DA, LS-SVM, DNN, and RP-CNN models created using three groups and two groups on day 14 during the garlic bulb enlargement period. When examining the accuracy of the models in the control group and heat level 1, it was found that among the three-group models, the LS-SVM model achieved the highest accuracy, while the other models showed no significant difference. Among the two-group models, the LS-SVM model also had the highest accuracy, with no significant difference observed among the remaining models. The PLS-DA, DNN, and RP-CNN models exhibited improved accuracy in the two-group models compared to the three-group models. Analyzing the accuracy of the models in the control group and heat level 2, it was found that among the three-group models, the LS-SVM model had the highest accuracy, followed by the PLS-DA model. The accuracy of the DNN and RP-CNN models did not significantly differ. For the two-group models, there was no significant difference in accuracy among all models.