# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 4 Results and Discussion
### Experimental Evaluation  
One of the tools used to evaluate the performance of supervised learning models trained for the classification of potato late blight disease was the confusion matrix. The matrix columns represent the labels predicted by the model, and the rows represent the true labels of the image. Figure 11 shows the confusion matrices of the CNN models. The CNN confusion matrix trained with the original data set comprises 64 test images corresponding to 20% of the original image set. It is observed that it correctly classified 56 images and 8 images with the wrong labels. Regarding the confusion matrix of the CNN trained with the augmented data set, it was created with a total of 448 images corresponding to 20% of the augmented data set, and it is observed that it correctly classified 417 images and 31 wrong label images. Figure 10: Prototype designs of obtained data visualization for administrators. Figure 12 shows the confusion matrices of the Support Vector Machine models. The SVM model confusion matrices were created from 128 test images. Of the 128 images tested, it is observed that the SVM model trained with color features correctly classified 112 images, the SVM model trained with texture features correctly classified 82 images; the SVM model trained with PCA features correctly classified 90 images with the correct labels, and the SVM model trained with combined features correctly classified 109 images. Based on the results obtained from the confusion matrices of the SVM and CNN models, the performance evaluation metrics of Equations (1)-(5) were calculated. Table 3 shows the percentage of correct predictions against each model's total data or the accuracy. CNN models had the best performance compared to SVM models. The CNN model trained on the augmented dataset achieved an accuracy of 93%, while the CNN trained on the original dataset achieved an accuracy of 87.5%. Within the SVM models, the model trained  
Figure 11: Confusion Matrices of the CNN Models. Figure 12: Confusion Matrices of Support Vector Machine (SVM) Models. with color features obtained better results with 87.5% accuracy, while the model trained with texture, PCA, and combined features obtained 64%, 70.3%, and 85%, respectively. Regarding the precision or quality of the models in the classification of the leaves, it is observed that the CNN trained with the increased data set achieves the highest precision with 92.9% in classifying leaves with the disease. Regarding the SVM models, the highest accuracy that can be achieved is 86.1% when training with the color characteristics. The sensitivity or capacity of the models to correctly detect and classify leaves infected with late blight is shown in column 5 of Table 3. It is observed that the CNN model trained with the increased data set presents better performance with 93.3% of sensitivity; that is, this model can detect and identify 93 out of 100 images infected with late blight. In turn, the SVM model trained with color features has 88.8% sensitivity, respectively. Additionally, the models' specificity or capacity to detect the negative samples, that is, the leaves that do not present late blight damage, is evidenced. The specificity of the CNN model trained with the augmented data set is 93.2%, which indicates that its ability to discriminate the cases where the leaves do not present late blight infection is high, while the SVM model trained with characteristics color has 88.8% specificity. The ROC curve shows the relationship between the true and false positive rates. Table 4 shows the area under the curve of each ROC curve corresponding to each trained model. Figure 13 shows the ROC curves corresponding to the CNN models trained with the original and augmented data sets, respectively. Compared to the evaluation carried out with the ROC curves, it can be seen that the convolutional neural network models stand out from the others, based on the fact that the CNN models obtained an AUC greater than 0.95.