# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 3 Results and Discussion
### MobileNet Augmented Experiments Results  
Table 3 shows the numerical evaluation of MobileNet's performance when training with different types of data enrichment. When trained with non-augmented original data, the model achieved a high accuracy (0.91), precision (0.92), recall (0.90), and F1 score (0.91). When data augmentation was applied to the original color dataset, its accuracy and recall decreased to 0.73 and 0.57, respectively, but its precision stayed high at 0.88. On the contrary, applying targeted data augmentation to the original images led to better performance. The accuracy and F1 score increased to 0.92, which indicates that MobileNet can effectively learn from variations added by targeted data augmentation. When using original, non-augmented data, MobileNet performed marginally worse on grayscale images. It achieved an accuracy and F1 score of 0.89. When using a dataset with grayscale augmented images, the accuracy decreased to 0.74 and the recall decreased to 0.61. However, applying targeted data augmentation to grayscale images showed superior performance, surpassing all other variations with an accuracy of 0.95, a precision of 1.0, and an F1 score of 0.95. It also generated the lowest test loss at 0.17. These results are supported by the loss metrics in Figure 5, which shows that the model is highly efficient when using that parametric combination. Figure 3: The accuracy and loss curves for all models on training and validation—original data. Figure 4: The accuracy and loss curves for all models on training and validation—grayscale data. Figure 5: The accuracy and loss curves for all MobileNet configurations on training and validation data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Color** & **Data Augmentation** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Test Loss** \\ \hline \multirow{3}{*}{Original} & Without augmentation & 0.91 & 0.92 & 0.90 & 0.91 & 0.19 \\  & Standard augmentation & 0.73 & 0.88 & 0.57 & 0.69 & 0.58 \\  & Targeted augmentation & 0.92 & 0.98 & 0.86 & 0.92 & 0.21 \\ \hline \multirow{3}{*}{Grayscale} & Without augmentation & 0.89 & 0.88 & 0.90 & 0.89 & 0.22 \\  & Standard augmentation & 0.74 & 0.86 & 0.61 & 0.71 & 0.47 \\  & Targeted augmentation & **0.95** & **1.00** & **0.90** & **0.95** & **0.17** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MobileNet performances on different color and data enrichment combinations (the best values for each metric are bolded). Figure 5 illustrates how well-targeted data augmentation, particularly in grayscale, performs in terms of accuracy and loss measures. The added variety and complexity that targeted data augmentation to the data brings may be the cause of performance increases. The lack of color depth and variance in the grayscale data could be the reason why the model achieved better results, allowing the model to focus more on the structural features without the distraction of color variations. Confusion matrices for both the original and grayscale datasets are shown in Figure 6. Their results indicate that while data augmentation is frequently used to reduce overfitting and boost a model's resilience to fresh data, the efficiency of this approach varies by the task and how it is implemented. When generated properly, targeted data augmentation seems to work especially well for tasks that demand high specificity and sensitivity since they may more accurately represent the key traits of the classes. With a low initial increase in the false positive rate (FPR) and a large true positive rate (TPR) gain, all receiver operating characteristic (ROC) curves rise quickly from their origin, as seen in Figure 7. This suggests that all configurations of the model perform well on the classification task. All configurations show strong classification abilities, as indicated by curves that consistently stay well above the diagonal line.