# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 5 Implementation and Testbed
Another limitation is that high-performance hardware is required in order to process the models. Table 4 shows the classification results for four categories that represent the olive leaf images (Aculusolearius, olive scab, peacock spot, and healthy) obtained from the ResNet50, MobileNet, and MobiRes-Net models. Classification was studied from four perspectives: accuracy, F1-score, precision, and recall. As shown in Table 4, ResNet50 achieved an accuracy of 93.25% for the Aculus olearius category, 91.45% for the olive scab category, 91.75% for the peacock spot category, and 96.23% for the healthy category. The highest recall (93.77%), precision (93.23%) and F1-score (93.89%) are for the healthy category. In \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline
**ResNet50** & 92.86 & 92.75 & 92.08 & 92.78 & 890 & 2.29 \\
**MobileNet** & 94.63 & 93.26 & 93.12 & 94.16 & 940 & 1.79 \\
**MobiRes-Net** & 97.08 & 97.61 & 97.11 & 96.86 & 1138 & 4.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average classification results of the original dataset. addition, MobileNet also attained the highest accuracy (97.98%) for the healthy category, the highest precision (94.71%) for the peacock spot category, the highest recall (93.75%) for the peacock spot category, and the highest F1-score (94.55%) for the Aculus olearius category. However, it can be noticed that MobiRes-Net achieved an accuracy of 96.25% for the Aculus olearius category, 96.45% for the olive scab category, 96.75% for the peacock spot category, and 98.23% for the healthy category. The highest recall (98.15%), precision (98.45%) and F1-score (97.23%) are for the healthy category. The confusion matrix for the ResNet50, MobileNet, and MobiRes-Net models used in the study include the findings that have been obtained from the training carried out with the test dataset presented in Figures 11-13. \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline
**Model** & **Category** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{4}{*}{**ResNet50**} & Aculus olearius & 93.25 & 91.25 & 91.55 & 91.36 \\  & Olive scab & 91.45 & 91.45 & 91.85 & 91.57 \\  & Peacock spot & 91.75 & 91.75 & 91.35 & 91.03 \\  & Healthy & 96.23 & 93.23 & 93.27 & 93.89 \\ \hline \multirow{4}{*}{**MobileNet**} & Aculus olearius & 94.15 & 94.52 & 92.25 & 94.55 \\  & Olive scab & 94.52 & 93.63 & 93.15 & 93.85 \\ \cline{1-1}  & Peacock spot & 93.01 & 94.71 & 93.75 & 93.75 \\ \cline{1-1}  & Healthy & 97.98 & 93.05 & 92.95 & 93.63 \\ \hline \multirow{4}{*}{**MobiRes-Net**} & Aculus olearius & 96.25 & 97.35 & 97.27 & 96.78 \\ \cline{1-1}  & Olive scab & 96.45 & 97.85 & 97.48 & 97.05 \\ \cline{1-1}  & Peacock spot & 96.75 & 96.45 & 96.72 & 96.75 \\ \cline{1-1}  & Healthy & 98.23 & 98.45 & 98.15 & 97.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model classification results for the four-category classification task for the original dataset. Figure 8: Accuracy and loss performance of the ResNet50 model. Figure 10: Cont. Figure 9: Accuracy and loss performance of the MobileNet model. Figure 11: Confusion matrix for the ResNet50 model. Figure 10: Accuracy and loss performance of the proposed MobiRes-Net model. Figure 12: Confusion matrix for the MobileNet model. Figure 13: Confusion matrix of the proposed MobiRes-Net model.