## 3 Experimental Results
### Performance Measures
To gain clearer perspective of classification findings, we employed confusion matrix plots and ROC curves (receiver operator characteristic) to depict the distinct crop binary classification results with distinct ML and DL algorithms. Figure 16 illustrates the confusion matrix graphs of the six machine learning models, as well as the five DL networks. In a confusion matrix chart, the abscissa represents the predicted label, and the ordinate represents the actual label. The confusion matrix diagonal contains the correctly classified instance data, and the values over/under the diagonal are the misclassified instances. As depicted in Figure 16, with the eleven machine and deep learning models, there were many instances (<300) where diseased leaves were misclassified as healthy (False Negative) on a test set of 8727. For the RF algorithm, the number of diseased crop leaves misclassified as healthy is 90. On the other hand, the number of healthy crop leaves misclassified as diseased is 110. Thus, the number of misclassified leaves is 200 (FN + FP) with an accuracy reaching 97.54%, which demonstrates its reliability in plant disease classification compared to other ML algorithms such as the NB algorithm, where 260 diseased leaves were misclassified as healthy. The results revealed that the NB algorithm is the least recommended for leaf disease classification regarding accuracy and FN. Regarding the deep learning networks, the InceptionV3 network was found on top as the number of leaves that were diseased but predicted to be healthy (FN) was 204, and otherwise for the FP was 100 so the total number of misclassified leaves (FP + FN) was 304 out of a dataset of 8727. So, we are keen to minimize the incidence of misclassifying diseased leaves, since it is a genuine threat when the classifier classes them as healthy, especially in the presence of a contagious disease that spreads swiftly through the field. Respectively for the DL models, we found that the InceptionV3 model is the best one for classifying diseases since not only does it produce a very high accuracy of 98.01% but it can also properly classify the leaves wherein the number of misclassified diseased leaves is 204. In contrast, the ResNet50 model yielded a great accuracy of 93.57% except that the number of misclassified diseased leaves (1748 incorrect predictions) is extremely high compared to the InceptionV3 architecture. This implies that studies based only on prediction are insufficiently reliable to assess the efficiency of a classifier. Then, what might be the root cause of this phenomenon? Looking at the experimental dataset, we observed that the characteristics of crop leaf diseases, including leaf mold and bacterial spot and downy mildew diseases, were quite close to each other, which could explain this phenomenon. * The ROC curve (Receiver Operating Characteristics Curve) [64] is a powerful tool for evaluating the performance of an ML/DL model. The ROC is applied to binary classification tasks where the output is composed of two distinct classes, and it represents a probability curve plotting the _TPR_ against _FPR_ at different threshold values and basically separates \"signal\" from \"noise\". Otherwise, it expresses sensitivity as a function of 1- specificity for all possible threshold values of the marker studied. So, it demonstrates a trade-off phenomenon between specificity and sensitivity. Indeed, Sensitivity is the ability of the test to detect diseased leaves correctly, and specificity is the capacity of the test to detect healthy leaves correctly. This underscores the affectability of the classifier model. Where False Positive Rate (FPR) indicates the ratio of the negative class that was incorrectly classified by the classifier. Formally, it is expressed as below:  
Figure 16: Confusion matrixes for the ML and DL algorithms. \[FPR=\frac{FP}{TN+FP}=1-\textit{Specificity}\]  
where True Positive Rate (\(TPR\)) indicates the proportion of the positive class that was properly classified by the classifier.