# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 7 Conclusions and Future Work  
The growing of olive trees is an essential economic activity in various countries, including The Kingdom of Saudi Arabia. However, the presence of plant diseases has affected regular production in a very negative manner. Recently, with the advantages of automatic DL and feature extraction, these techniques have received increased interest from academic and industrial circles. Simultaneously, it has become a hotspot for further research in the field of agricultural plant protection, namely the recognition of plant diseases. This paper is based on experimental work using a remote-controlled agricultural UAV, where it moves autonomously in an olive farm. The UAV is equipped with a camera and coupled with the hybrid DL technique it moves autonomously in olive farms for detecting and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multicolumn{2}{c}{**CNN Networks**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**F1-Score (\%)**} \\ \hline
[11] & AlexNet & PlantVillage & 99.11 & 92.75 & 92.08 & 92.28 \\ \hline
[12] & VGG16 & \multirow{2}{*}{PlantVillage} & 98.6 & 92.26 & 93.12 & 92.25 \\  & ResNet & & 97.5 & 96.61 & 97.11 & 96.86 \\ \hline
[13] & VGG-16 & \multirow{2}{*}{
\begin{tabular}{c} Olive leaf \\ VGG-19 \\ Proposed \\ model \\ \end{tabular} } & 89.12 & 92.75 & 92.08 & 92.28 \\  & & 88.5 & 92.26 & 93.12 & 92.25 \\  & & 95.63 & 96.61 & 97.11 & 96.86 \\ \hline
[14] & Modified & \multirow{2}{*}{Collected} & \multirow{2}{*}{97.12} & \multirow{2}{*}{92.75} & \multirow{2}{*}{92.08} & \multirow{2}{*}{92.28} \\  & ResNet50 & & & & \\ \hline \multirow{4}{*}{[15]} & AlexNet & 99.06 & 92.26 & 93.12 & 92.25 \\  & AlexNetOWTBn & & 99.44 & 96.61 & 97.11 & 96.86 \\  & GoogleNet & Collected & 97.27 & 92.75 & 92.08 & 92.28 \\  & Overfeat & & 98.96 & 92.26 & 93.12 & 92.25 \\  & VGG & & 99.48 & 96.61 & 97.11 & 96.86 \\ \hline
[16] & Inception & \multirow{2}{*}{Collected} & \multirow{2}{*}{95.91} & \multirow{2}{*}{92.75} & \multirow{2}{*}{92.08} & \multirow{2}{*}{92.28} \\  & AlexNet & & & & & \\  & ResNet50 & & 94.12 & 96.61 & 97.11 & 96.86 \\  & ResNet-v2 & & 91.81 & 92.75 & 92.08 & 92.28 \\ \hline \multirow{4}{*}{[17]} & AlexNet, & \multirow{2}{*}{
\begin{tabular}{c} PJM34 \\ VGG16, \\ ResNet. \\ \end{tabular} } & 87.34 & 92.26 & 93.12 & 92.25 \\  & VGG16, & & 92.87 & 96.61 & 97.11 & 96.86 \\  & Inception-v3 & PlantVillage & 94.32 & 92.75 & 92.08 & 92.28 \\  & ResNet. & & 92.56 & 92.26 & 93.12 & 92.25 \\  & Proposed & & 97.87 & 96.61 & 97.11 & 96.86 \\ \hline
[18] & ResNet101 & PlantVillage & 96.46 & 92.75 & 92.08 & 92.28 \\ \hline
[19] & AlexNet & \multirow{2}{*}{Collected} & \multirow{2}{*}{90.12} & \multirow{2}{*}{92.26} & \multirow{2}{*}{93.12} & \multirow{2}{*}{92.25} \\  & Resnet & & & & & \\  & DenseNet & & 96.25 & 96.61 & 97.11 & 96.86 \\ \hline
**Our proposed** & \multirow{2}{*}{
\begin{tabular}{c} ResNet50 \\ MobileNet \\ MobiRes-Net \\ \end{tabular} } & 98.35 & 92.75 & 92.08 & 92.28 \\  & MobiRes-Net & Collected & 94.63 & 93.26 & 93.12 & 94.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results obtained as compared with state-of-the-art methods. classifying olive leaf diseases. Then, the captured images are transmitted to the Cloud for identification of olive leaf diseases using deep learning techniques. To help detect olive leaf diseases, a hybrid DL model is proposed in this work. It is an amalgamation of the ResNet50 and MobileNet models, and is given the name MobiRes-Net. The proposed model achieved a classification accuracy of 97.08%, which was superior rate as compared with the ResNet50 and MobileNet models whose classification accuracies were, respectively, 92.86% and 94.63%. Since nothing can be perfect, our proposed MobiRes-Net model has some limitations. One limitation is the higher training and testing run time as compared with other models because of the complex structure of the models inside modules.