# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 3 Results and Discussion
### MobileNet Augmented Experiments Results
This suggests that the MobileNet model retains a basic level of efficiency in drought detection, regardless of the data augmentation techniques used and the subtle differences in efficiency these techniques lead to. The ROC curve of the grayscale targeted augmented dataset, with the highest area under the curve (AUC) at 0.99, is the best performer, indicating that the model with this configuration provides excellent discrimination between the positive and negative classes with minimal error. On the other hand, the curve of the original augmented dataset, with the lowest AUC at 0.90, is the least effective but still shows good performance. One of the primary concerns in this research was the small size and limited diversity of our dataset, which could impact the model's ability to adapt to different real-world problems. Even under these challenging conditions, DenseNet, InceptionV3, and MobileNet  
Figure 6: Confusion matrix for MobileNet configurations on the test data: (**a**) original non-augmented data; (**b**) original standard augmentation; (**c**) original targeted augmentation; (**d**) grayscale non-augmented data; (**e**) grayscale standard augmentation; (**f**) grayscale targeted augmentation. demonstrated their capability to recognize if the plant is exposing drought stress with an accuracy between 0.85 and 0.93 and an F1 score between 0.87 and 0.93. Our findings reveal that MobileNet provided the most efficient, stable, and reliable performance. DenseNet and InceptionV3 also produced good results but lacked consistency across different data formats and took a longer time to train. When it comes to the application of data augmentation, it was shown that standard data augmentation did not improve the model's performance, while targeted data augmentation improved the results. Applying targeted data augmentation on grayscale images proved to be effective, demonstrating improved results, with an F1 score and an accuracy of 0.95 for MobileNet. This can be explained by the fact that this kind of data augmentation addressed the distinctiveness in image characteristics between classes by adding features that were initially absent in drought-stressed classes. These results show that the use of targeted data augmentation can help address the problem of data limitation, which is a significant concern in AI-based agricultural applications. Moreover, we determined that using grayscale images produced better results, especially when combined with targeted data augmentation. This could be due to reduced complexity in the grayscale image data, allowing the model to focus more on the structural features without the distraction of color variations. The results acquired using the proposed methodology are binary, meaning that they indicate whether or not a plant is exposed to drought stress, making them simple for agricultural experts to comprehend. The suggested approach might be used to automatically determine which inbred lines grown under controlled conditions show lower drought tolerance. In that way, the obtained results provide a basis for making informed choices regarding which inbred lines are more resilient and suitable for specific environmental conditions. For more effective management of agricultural resources when it comes to field-grown plants, this information can be crucial when making decisions about the use of irrigation, which is especially important in seed production. This, in consequence, would  
Figure 7: Receiver operating characteristic (ROC) curve for MobileNet configurations: (**a**) original non-augmented data; (**b**) original standard augmentation; (**c**) original targeted augmentation; (**d**) grayscale non-augmented data; (**e**) grayscale standard augmentation; (**f**) grayscale targeted augmentation. enable farmers to use this insight to more effectively manage water resources and prioritize care for those lines.