# Plant Disease Detection Using Deep Convolutional Neural Network
## 4 Results
\[\text{Classification accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+ \text{FP}+\text{FN}} \tag{9}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
\[\text{F1 Score}=2\times\frac{(\text{precision}\;\times\;\text{recall})}{(\text {precision}\;+\;\text{recall})} \tag{12}\]  
The range of the classification accuracy, precision, recall and F1 score is between 0 (0%) and 1 (100%). The classification performance of the proposed 14-DCNN model on individual classes in the dataset is illustrated in Table 5.  
Figure 7 compares the performance of the proposed 14-DCNN and existing state-of-the-art classification techniques using accuracy, weighted average precision, weighted average recall and weighted average F1 score. The comparison results show the accuracy, precision, recall, and F1 scores of the proposed 14-DCNN are higher than the AlexNet, Inception-v3-Net, ResNet-50 and VGG16 Net. Additionally, the complexity of the proposed 14-DCNN and other transfer learning techniques is illustrated in Figure 3:0  
The complexity analysis result shows that the number of trainable parameters and model size of the proposed 14-DCNN model are lesser than the existing transfer learning techniques. The smaller number of trainable parameters and small model size will reduce the complexity of the model prediction process. The comparison results illustrate that the performance of the proposed 14-DCNN is higher than the AlexNet, Inception-v3-Net, ResNet-50 and VGG16 Net on plant leaf disease classification. Figure 3:1 Sample AUC-ROC curves of 14-DCNN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plant Name** & **Class Names** & **PRECISION** & **RECALL** & **F1-SCORE** \\ \hline \multirow{3}{*}{Aloe Vera} & Healthy & 1 & 1 & 1 \\  & Leaf Rot & 1 & 0.98667 & 0.99329 \\  & Leaf Rust & 0.98684 & 1 & 0.99338 \\ \hline \multirow{3}{*}{Apple} & Healthy & 1 & 1 & 1 \\  & Leaf Scab & 1 & 1 & 1 \\  & Black Rot & 1 & 0.98667 & 0.99329 \\  & Leaf Rust & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Banana} & Healthy & 1 & 1 & 1 \\  & Bacterial Witl & 0.99338 & 1 & 0.99668 \\  & Black Sigatoka & 1 & 0.99333 & 0.99666 \\ \hline \multirow{3}{*}{Cherry} & Healthy & 1 & 1 & 1 \\  & Powder & 1 & 1 & 1 \\  & Mildew & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Citrus} & Healthy & 1 & 1 & 1 \\  & Black Spot & 0.98684 & 1 & 0.99338 \\ \cline{1-1}  & Canker & 1 & 1 & 1 \\  & Greening & 1 & 1 & 1 \\  & Melanose & 1 & 0.98667 & 0.99329 \\ \hline \multirow{3}{*}{Corn} & Healthy & 1 & 1 & 1 \\  & Common Rust & 1 & 1 & 1 \\  & Leaf Spot & 1 & 1 & 1 \\  & Northern Leaf & 1 & 1 & 1 \\  & Blight & & & \\ \hline \multirow{3}{*}{Coffee} & Healthy & 1 & 1 & 1 \\  & Cercospora Leaf & 1 & 1 & 1 \\  & Spot & 1 & 1 & 1 \\  & Leaf Rust & 1 & 1 & 1 \\  & Red Spider Mite & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Grape} & Healthy & 1 & 1 & 1 \\  & Black Measles & 1 & 0.98 & 0.9899 \\  & Black Rot & 0.96774 & 1 & 0.98361 \\  & Leaf Blight & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Paddy} & Healthy & 1 & 1 & 1 \\  & Brown Spot & 0.98039 & 1 & 0.9901 \\  & Hispa & 1 & 1 & 1 \\  & Leaf Blast & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Peach} & Healthy & 1 & 1 & 1 \\  & Bacterial Spot & 0.98658 & 0.98 & 0.98328 \\ \hline \multirow{3}{*}{Pepper} & Healthy & 1 & 1 & 1 \\  & Bacterial Spot & 1 & 0.98667 & 0.99329 \\ \hline \multirow{3}{*}{Potato} & Healthy & 1 & 1 & 1 \\  & Early Blight & 1 & 1 & 1 \\  & Late Blight & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Strawberry} & Healthy & 1 & 1 & 1 \\  & Leaf Scorch & 1 & 1 & 1 \\ \hline \multirow{3}{*}{Tea} & Healthy & 1 & 1 & 1 \\  & Leaf Blight & 1 & 0.98667 & 0.99329 \\ \cline{1-1}  & Red Leaf Spot & 0.98684 & 1 & 0.99338 \\ \cline{1-1}  & Red Scab & 1 & 1 & 1 \\ \hline \end{tabular}
\end{table} 
Table 5: Class-wise performance of proposed 14-DCNN model.