# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 4 Results and Discussion
### Evaluation Measure
#### 4.1.4 Kappa-Cohen Index
The accuracy value was lower than that of the other three classes where precision and specificity were 100% in this case. The global or overall results for the all classes had a primary issue. Accuracy was 97.11%, lower than that of AlexNet and GoogLeNet, where specificity was lower than that of both AlexNet and GoogLeNet, and the F1 score, precision, and kappa were lower for this model testing the data predictions. The kappa index showed promise for these data. ResNet-50 and -101 had much more improved results than those of AlexNet, SqueezeNet, and GoogLeNet. ResNet-50 showed cancer class results of 99.51 accuracy, 99.68% specificity, a 99.28% of F1 score, a 99.03% precision value, and 0.51 for precision value of the kappa index. Compared to the previous cases of AlexNet, GoogLeNet, and SqueezeNet, the results were overall improved for all measures. Similarly, for the dot class, the accuracy value was 100%, 99.697% specificity, a 99.515% F1 score, a 99.034% precision value, and a 0.52 kappa index. The cancer class results were not better than those of the dot class if we look at the accuracy measures, with only a slight difference in the F1 scores. Mummification results showed 98.661% accuracy, 100% specificity, a 100% F1 score, 98.25% precision, and a 0.48 kappa value. Although it had 100% accuracy as an individual class, the specificity and precision values were also higher than those of the cancer and dot classes. The rust class showed 100% accuracy, 100% specificity, a 100% F1 score, and a 100% precision value. The overall results were improved as compared to the above models' mean results. The global mean results were 99.54% accuracy, which was better than that of SqueezeNet, GoogLeNet, and AlexNet. Specificity was also better than that in the three models. The F1 score and precision were both better than those in the previously discussed three models. Although it had a higher value than that of the previous models, the last kappa had the same class of confidence. The data reliability was higher for all models. The last model of the proposed study also achieved good results as compared to the other models. The darker class showed predictivity values of 99.519% accuracy, 98.784% specificity, an F1 score of 97.87%, a precision of 96.27%, and a 0.51 kappa index. ResNet-50's accuracy had a dominant result compared to the previous class results of the other models, while other evaluation values also showed more improvement in this model. If the accuracy value was improved, other values were not so improved, but different cases showed overall improvement of the results. In ResNet-101, the dot class showed 98.049% accuracy, 99.84% specificity, 99.505% precision, and a 0.53 kappa value. Accuracy, specificity, precision, and the F1 score were overall improved for each class, which did not happen in the previous models' results for any class. The mummification class again showed 100% accuracy in this model testing. The mummification class showed 100% accurate results in other models where other values were not improved to such an extent: 98.41% accuracy, 99.84% specificity, 98.93% F1 score, and 99.47% precision. The last class showed consistency in the improvement of the results for each class by also showing promising results here. Lastly, the overall mean results of ResNet-50 proved it to be more accurate than the four other models. The accuracy was also better than that of the others. Similarly, other measures also showed excellent performance. The graphical illustration of all five models' mean testing results is shown in Figure 4.  
The overall results showed that the kappa value had overall excellent data reliability for all models. The mummification, the healthy and dot classes were more distinct classes to distinguish them from the other two classes, as they showed 100 true results many times. The densest model with more residual connections showed more accurate results, which means that using a small kernel size with an increasingly denser network improved the classification results.