# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 3 Results and Discussion
### Initial Experiments Results  
Five different CNN architectures processed in both original and grayscale formats were compared, as presented in the quantitative evaluation of models in Table 2. The performance of the MobileNet architecture was demonstrated by its accuracy and F1 score, which were 0.91 for the original color format. With an accuracy and F1 score of 0.89, this model was also effective in grayscale. DenseNet achieved the best grayscale accuracy and F1 score of 0.93 on grayscale images, surpassing its first color image accuracy of 0.85 and F1 score of 0.87. Likewise, InceptionV3 achieved high performance for the grayscale images--it achieved an accuracy and an F1 score of 0.93. However, the performance of the original color did not remain that high. While the VGG19 model performed slightly better in grayscale, the VGG16 and VGG19 models demonstrated moderate to lower performance metrics in comparison with other models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CNN Model** & **Number of Epochs** & **Learning Rate** \\ \hline VGG16 & 50 & 0.0001 \\ \hline VGG19 & 30 & 0.0001 \\ \hline DenseNet & 30 & 0.0001 \\ \hline Inception V3 & 30 & 0.0001 \\ \hline MobileNet & 30 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimal number of epochs and learning rate for the used models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CNN Model** & **Color** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Test Loss** & **Average Epoch Time (s)** \\ \hline \multirow{2}{*}{VGG16} & Original & 0.82 & 0.81 & 0.86 & 0.84 & 0.82 & 250.81 \\  & Grayscale & 0.65 & 0.62 & 0.84 & 0.72 & 0.65 & 233.69 \\ \hline \multirow{2}{*}{VGG19} & Original & 0.66 & 0.91 & 0.39 & 0.55 & 0.66 & 306.31 \\  & Grayscale & 0.67 & 0.81 & 0.49 & 0.61 & 0.67 & 282.62 \\ \hline \multirow{2}{*}{DenseNet} & Original & 0.85 & 0.78 & **1.00** & 0.87 & 0.85 & 103.00 \\  & Grayscale & **0.93** & **0.98** & 0.88 & **0.93** & 0.93 & 90.91 \\ \hline \multirow{2}{*}{Inception V3} & Original & 0.85 & 0.78 & **1.00** & 0.87 & 1.61 & 69.16 \\  & Grayscale & **0.93** & 0.94 & 0.92 & **0.93** & **0.13** & 51.28 \\ \hline \multirow{2}{*}{MobileNet} & Original & 0.91 & 0.92 & 0.90 & 0.91 & 0.19 & 56.60 \\  & Grayscale & 0.89 & 0.88 & 0.90 & 0.89 & 0.22 & **33.29** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of performances for all models (the best values for each metric are bolded).  
The average epoch time for training these models varied significantly. MobileNet was the most time efficient at 56.6 s for original and 33.29 s for grayscale images, suggesting an advantage in processing speed without sacrificing model accuracy and reliability.  
We further analyzed model performance by plotting the training accuracy and loss curves for all models on the training and validation datasets. Figures 3 and 4 show the accuracy and loss progress for each model over the epochs for the original and grayscale images. The VGG models, VGG16 and VGG19, performed the worst across both datasets and showed low stability. MobileNet achieved high accuracy, stability, and minimal divergence, and its learning curves showed a robust and steady learning pattern. Inception V3 performed inconsistently, with noticeable variations in validation accuracy and a high loss in the original dataset. DenseNet showed high accuracy, and it maintained a low and stable loss curve. However, MobileNet had the shortest training time and the most consistent and convergent validation loss and accuracy over epochs. It was chosen for subsequent experiments due to its stable performance, quick training duration, and consistent results.