## 3 Experimental Results
### Performance Measures  
The evaluation of the trained models' output was elaborated using several performance measures, namely f1-score, accuracy, recall, and precision, as reported in Table 4, and AUC (Area Under ROC), which is determined by the true negatives (TN), true positives (TP), false negatives (FN), and false positives (FP) obtained from the confusion matrix. * _Precision_: It depicts in binary classifications all the positive classes predicted correctly by the model; how many of them are positive. It is computed by dividing the number of correctly classified positive samples by the number of predicted positive examples. The formula is expressed as follows: \[Precision=\frac{TP}{TP+FP}\]
* _Recall_ /or _sensitivity_[61]: It sets the number of correctly predicted samples among all the positive classes. The equation is stated as below: \[Recall/sensitivity=\frac{TP}{TP+FN}\]
* \(F1\)-_score_[62]: The F1 score yields a global estimate of a test subject's recall and accuracy. It refers to the harmonic average of recall and precision. Formally, the F1 score is determined by the following: \[F1-score=2*\frac{Precision*Recall}{Precision+Recall}\]
* _Accuracy_: This is a metric for assessing classification models. It is a fraction of all correct predictions. Formally, it is expressed as below: \[Accuracy=\frac{No\ of\ correct\ Precisions}{Total\ no\ of\ Precisions}\]  
These various performance metrics are applied to predict the outcome of the different models, and a list of them is presented in Table 6. From this exhibit Table, we can see that the metrics of the InceptionV3 network outperformed all the measured machine learning algorithms. For instance, the recall of the tested machine learning algorithms was 92.26% (KNN), 84.37% (SVM), and 97.47% (RF), while it was 97.53% (InceptionV3), 91.64% (ResNet50), 91.39% (CNN), and 90.17 (VGG16) for the evaluated deep learning networks. To be noticed, we are highly concerned about the sensitivity/recall, which calculates the percentage of true positives (diseased leaves). For example, if a leaf infected (true positive) is predicted to be healthy (predicted negative), the consequences will be extremely  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline InceptionV3 & 98.01\% & 96.52\% & 97.53\% & 96.52\% \\ CNN & 93.89\% & 91.57\% & 91.39\% & 91.32\% \\ ResNet50 & 93.57\% & 92.41\% & 91.64\% & 91.69\% \\ VGG16 & 87.50\% & 90.43\% & 9  
onerous. All evaluated metric values were superior to 60.09% of the tested machine learning algorithms and superior to 86.70% of the tested deep learning models. The classification outcomes of the RF classifier were the best among the six machine learning algorithms examined, followed by the CART classifier and lastly the KNN classifier. Concurrently, the ranking order of the classification outcomes of the tested deep learning models, from top to bottom, is InceptionV3, CNN from scratch, ResNet50, VGG16, and finally VGG19. * The confusion or error matrix [63]: It describes the classifier's performance on the test data. It helps to identify and pinpoint each cluster that might have been misclassified by the classifier and to further improve the proposed classification model in the future. Each row represents the predicted class examples, and each column of the matrix represents the actual class examples. Thus, we computed the confusion matrix of each model to compare the different algorithms, as it allows us to measure the degree of classification model accuracy for each category. As we are dealing with a binary classification of leaf disease, we are interested in false negatives (FN). This is also called \"type 2 error\", which represents the rate of misclassified crop leaves that appear healthy but are affected by diseases, thereby presenting a severe threat to the crop, especially if it concerns a viral disease that will spread swiftly over the field.