# Classification of Apple Disease Based on Non-Linear Deep Features
## 4 Conclusions  
This study proposed a DCGAN-DCNN model for apple disease, i.e., Blotch, Rot, and Scab classification. The DCNN structure consists of five convolutional, two dense, and one decision vector layer to classify the apple disease. Experimental results reveal that the proposed model outperformed several conventional and state-of-the-art deep models. However, the learning rate and optimizer have a strong influence; therefore, an appropriate selection of these two essential hyper-parameters is critical to get better results. Future research entails incorporating the soft and hard attentional mechanism in deep models for apple disease classification.  
Methodology and software, H.A. and E.R.-E. Formal analysis, M.A. and D.O. Investigation, M.P.-C. and R.S. Supervision, M.A., D.O., M.P.-C. and R.S. Validation, M.A. and D.O. Writing, review and editing, H.A., E.R.-E., M.A., M.P.-C. and R.S. All the authors have read and agreed the last version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Blotch** & **Scab** & **Rot** & **Healthy** & **OA(\%)** \\ \hline \multicolumn{6}{c}{**State-of-the-art and Conventional Methods**} \\ \hline
2D CNN & 99.00\% & 99.99\% & 91.00\% & 99.99\% & 99.17\% \\ ResNet50 & 99.99\% & 99.00\% & 76.00\% & 99.99\% & 96.00\% \\ MiniVggNet & 99.99\% & 60.90\% & 20.00\% & 84.42\% & 69.00\% \\ SqueezeNet & 99.99\% & 99.99\% & 91.00\% & 99.99\% & 99.00\% \\ ISADH+GLCM+MSVM & 99.99\% & 98.57\% & 99.90\% & 95.71\% & 96.00\% \\ GCH+LBP+MSVM & 88.46\% & 85\% & 89.72\% & 86.66\% & 90.80\% \\ CCV+CLBP+ZM+MSVM & 97.50\% & 93.75\% & 92.50\% & 99.90\% & 95.60\% \\ HSV+CLBP+MSVM & 89.88\% & 90.71\% & 96.66\% & 99.33\% & 93.00\% \\ \hline \multicolumn{6}{c}{**Proposed Method without synthetic examples**} \\ \hline DCNN-Adam & 99.99\% & 99.99\% & 99.99\% & 99.99\% & 99.99\% \\ DCNN-Adamax & 93.00\% & 93.00\% & 99.99\% & 99.99\% & 96.66\% \\ DCNN-Nadam & 99.99\% & 86.66\% & 86.66\% & 99.99\% & 93.00\% \\ DCNN-Adadalta & 99.99\% & 99.99\% & 73.33\% & 86.66\% & 88.00\% \\ \hline \multicolumn{6}{c}{**Proposed Method with synthetic examples**} \\ \hline DCGAN-DCNN-Adam & 99.99\% & 99.99\% & 99.99\% & 99.99\% & 99.99\% \\ DCGAN-DCNN-Adamax & 99.00\% & 99.00\% & 99.99\% & 99.99\% & 99.66\% \\ DCGAN-DCNN-Adadalta & 99.99\% & 99.00\% & 99.00\% & 99.99\% & 99.00\% \\ DCGAN-DCNN-Nadam & 99.99\% & 98.00\% & 99.99\% & 99.99\% & 98.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental evaluation with and without synthetic examples as compared to the state-of-the-art as well as conventional methods. The higher accuracies are in bold face.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ResNet** & **SqueezeNet** & **MiniVGGNet** & **Proposed** \\ \hline Kernels & \(32,64,32,64,32,64,32\) & \(128,128,128,64,64,64,32,32,32\) & \(128,128,64,64\) & \(128,64,64,32,32\) \\ \hline Batch size & 55 & 55 & 55 \\ \hline Filters size & \((3\times 3)\), \((3\times 3)\) & \((1\times 1),(1\times 1),(3\times 3)\) & \((3\times 3)\) & \((3\times 3)\) \\ \hline Activation Function & relu & relu & relu \\ \hline Optimizer & Adam & Adam & Adam & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 3: Layer-wise settings of all comparative models.