# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \begin{tabular}{c} **CNN** \\ **Networks** \\ \end{tabular} & **Dataset** & \begin{tabular}{c} **Accuracy** \\ **(\%3)** \\ \end{tabular} & **Strength** & \begin{tabular}{c} **Weaknesses** \\ \end{tabular} \\ \hline \multirow{2}{*}{[11]} & AlexNet & PlantVillage & 99.11 & The work is robust to noisy samples & Takes time during the training process \\ \cline{2-6}  & VGG16 & PlantVillage & 98.6 & The approach requires less training data & Higher computational complexities \\  & ResNet & 97.5 & & \\ \hline \multirow{4}{*}{[13]} & VGG-16 & \multirow{4}{*}{\begin{tabular}{c} Olive leaf \\ Proposed \\ model \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} 89.12 \\ images \\ \end{tabular} } & VGG-19 & \multirow{4}{*}{\begin{tabular}{c} Olive leaf \\ images \\ \end{tabular} } & 89.12 & \multirow{4}{*}{\begin{tabular}{c} 88.5 \\ 95.63 \\ \end{tabular} } & Higher accuracy ratings & Higher computational complexities \\  & & & 95.63 & & \\ \cline{2-6}  & Modified & & & & \\ \cline{2-6}  & ResNet50 & Collected & 97.12 & Higher accuracy and faster detection speed & Takes higher execution time \\ \hline \multirow{4}{*}{[14]} & AlexNet & \multirow{4}{*}{\begin{tabular}{c} 99.06 \\ GoogleNet \\ Overfeat \\ VGG \\ VGG \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} 99.44 \\ 97.27 \\ VGG \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Reduced number of parameters with \\ 97.27 \\ VGG \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Results are reported for a small dataset \\ 99.48 \\ \end{tabular} } \\ \cline{2-6}
[16] & Inception & Collected & 95.91 & Improved accuracy in olive disease detection & Classification performance needs further improvements \\  & AlexNet & 89.90 & & \\  & ResNet50 & 94.12 & & \\  & ResNet5+2 & 91.81 & & \\ \hline \multirow{4}{*}{[17]} & AlexNet & \multirow{4}{*}{\begin{tabular}{c} 87.34 \\ VGG16 \\ ResNet \\ Proposed \\ model \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} 92.87 \\ 94.32 \\ 92.56 \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} The technique is computationally efficient \\ 92.56 \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Takes higher execution time \\ 92.56 \\ \end{tabular} } \\  & ResNet & 97.87 & & \\ \hline \hline
[18] & ResNet101 & PlantVillage & 96.46 & Fast, inexpensive, and deployable model & Requires a large amount of data to train a model \\ \hline \multirow{4}{*}{[19]} & AlexNet & \multirow{4}{*}{Collected} & 90.12 & Ability to reflect the similarity of the same type & Higher computational complexities \\  & Resnet & 96.25 & data itself & Higher computational complexities \\  & DensNet & 98.35 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of related works. by QQDS were applied in the proposed work and the classification rate was shown to be about 98.6%. [PERSON] et al. [13] proposed a deep convolutional neural network (DCNN) aimed at the classification of aculeus olearius diseases and olive peacock spot. A total of 3400 samples of olive leaves were chosen in which there were healthy leaves, olive peacock spot, and leaves affected by aculeus olearius. To accomplish the experimental study, transfer learning methods on VGG16 and VGG19 architectures were used. Based on the dataset, experiments were conducted in two major sections: with and without data augmentation. Furthermore, a web-based application of this study was developed. The findings showed that olive peacock spot and Aculus Olearius could both be recognized with low error rates even without the presence of an expert. To help detect and classify olive spot disease, [PERSON] et al. [14] proposed a method that used an analysis of leaf image textures. In the presented methodology, histogram thresholding was used to isolate the infected area, k-means segmentation, and texture features such as energy, homogeneity, and entropy.