# Plant Disease Detection Using Deep Convolutional Neural Network
## 4 Results  
This section examines the performance of the proposed 14-DCNN model, using various performance evaluation approaches and testing datasets. Additionally, the performance of the proposed 14-DCNN model is compared with other state-of-the-art techniques. The state-of-the-art techniques are AlexNet [25], Inception-v3-Net, ResNet-50 and VGG16 Net [7, 8]. These models were trained on a deep learning server system using the PlantDisease59 dataset and tested using the testing dataset. The proposed and existing mod  
Figure 4: Sample predicted images using the 14-DCNN model. Figure 3: (**a**) Accuracy and (**b**) loss of the proposed 14-DCNN model. els were trained only on face-up leaf images. So, the models will give the best performance on the face-up direction positioned images. Occlusion sensitivity can visualize the most important part of the input image for classification identified by the trained model. It can measure the sensitivity of the neural network to occlusion in different regions of the data, using small perturbations of the data. This region is known as the occluding region [7]. The white and light blue color pixels of the image illustrate the most essential parts of being classified into the expected class. The dark blue color region of the image has minimum features for the classification. Occluding stride and occluding size of the selected sensitivity map are 10 and 30, respectively. Figure 5 illustrates the occlusion sensitivity of the proposed 14-DCNN model on sample test data. The confusion matrix is a summary of predictions made by the classification techniques. The confusion matrix of the classification technique represents the true positive (TP), true negative (TN), false positive (FP) and false negative (FN) values of every single class [3]. The area under the receiver operating characteristic (AUC-ROC) curve is one of the popular metrics that is used to evaluate the performance of learning algorithms. The ROC curve plots the difference between the true positive rate (TPR) and false positive rate (FPR) [15]. The TPR and FPR are calculated using Equations (7) and (8). \[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{8}\]  
The AUC-ROC curve of the cherry healthy and strawberry leaf scorch classes shows the classification advantage of the proposed 14-DCNN. Figure 6 illustrates the AUC-ROC curve of the cherry healthy and strawberry leaf scorch classes, using the proposed 14-DCNN model. The performance of the proposed 14-DCNN model and state-of-the-art techniques is compared, using the most common performance metrics, such as classification accuracy, precision, recall and F1-score [3]. At first, the classification accuracy is defined as the correctly classified images divided by the total number of testing images. Precision is the second most important performance evaluation metric in classification techniques. Precision is defined as the number of correctly identified results divided by the number of correctly identified and correctly rejected results that are predicted by the model. Precision is used to find the correct proportion of the classification. Figure 5: (a) Original image, (b) occlusion sensitivity of 14-DCNN. The third most important performance evaluation technique is recall. The recall is the number of correctly identified results divided by the number of correctly identified and incorrectly rejected results. The recalls are used to determine the proportion of actual positives that were correctly identified. The F1 score is one of the widely used metrics for the performance evaluation of machine learning algorithms. The F1 score is defined as the harmonic mean between precision and recall. The F1 score value represents the prediction advantage of the classification techniques. The following Equations (9)-(12) were used to calculate the accuracy, weighted average precision, weighted average recall and weighted average F1 score of the classification techniques.