# 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
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Categories** & **Accuracy** & **Specificity** & **F1-Score** & **Precision** & **Kappa** \\ \hline AlexNet & Canker & 0.98077 & 0.99088 & 0.97608 & 0.97143 & 0.52096 \\ \hline  & Dot & 0.98049 & 0.99697 & 0.98529 & 0.99015 & 0.5309 \\ \hline  & Healthy & 1 & 1 & 1 & 1 & 0.90762 \\ \hline  & Mummification & 0.98661 & 0.99533 & 0.98661 & 0.99533 & 0.48509 \\ \hline  & Rust & 0.98942 & 0.99705 & 0.98942 & 0.98942 & 0.5648 \\ \hline  & Overall & 0.9850 & 0.9960 & 0.9875 & 0.9875 & 0.9531 \\ \hline GoogLeNet & Canker & 0.96635 & 0.99696 & 0.9781 & 0.99015 & 0.52859 \\ \hline  & Dot & 0.97561 & 0.98638 & 0.96618 & 0.95694 & 0.52695 \\ \hline  & Healthy & 1 & 1 & 1 & 1 & 0.90762 \\ \hline  & Mummification & 0.99429 & 0.99377 & 0.97297 & 0.98182 & 0.49202 \\ \hline  & Rust & 0.99471 & 0.99114 & 0.98172 & 0.96907 & 0.56 \\ \hline  & Overall & 0.9758 & 0.9937 & 0.9798 & 0.9796 & 0.9242 \\ \hline SqueezeNet & Canker & 0.97596 & 0.99392 & 0.97831 & 0.98068 & 0.52404 \\ \hline  & Dot & 1 & 1 & 0.96698 & 1 & 0.51541 \\ \hline  & Healthy & 1 & 1 & 1 & 1 & 0.90762 \\ \hline  & Mummification & 0.98214 & 0.9891 & 0.97561 & 0.96916 & 0.48364 \\ \hline  & Rust & 0.91534 & 1 & 0.9558 & 1 & 0.5866 \\ \hline  & Overall & 0.9711 & 0.9924 & 0.9753 & 0.9772 & 0.9098 \\ \hline ResNet-50 & Canker & 0.99519 & 0.99696 & 0.99281 & 0.99043 & 0.51957 \\ \hline  & Dot & 1 & 0.99697 & 0.99515 & 0.99034 & 0.52957 \\ \hline  & Healthy & 1 & 1 & 1 & 1 & 0.90762 \\ \hline  & Mummification & 0.98661 & 1 & 1 & 1 & 0.48733 \\ \hline  & Rust & 1 & 1 & 1 & 1 & 0.56351 \\ \hline  & Overall & 0.9954 & 0.9988 & 0.9962 & 0.9962 & 0.9856 \\ \hline ResNet-101 & Canker & 0.99519 & 0.98784 & 0.97872 & 0.96279 & 0.51484 \\ \hline  & Dot & 0.98049 & 0.99849 & 0.98772 & 0.99505 & 0.53178 \\ \hline  & Healthy & 1 & 1 & 1 & 1 & 0.90762 \\ \hline  & Mummification & 0.98214 & 1 & 1 & 1 & 0.48887 \\ \hline  & Rust & 0.98413 & 0.99557 & 0.98413 & 0.98413 & 0.56544 \\ \hline  & Overall & 0.9861 & 0.9964 & 0.9883 & 0.9884 & 0.9567 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained on the basis of individual class testing data. Specificity, which was 99.7% in the case of the dot class, represented TN among TN and FP where the precision was 99% for TP over TP and FP. The F1 score of this measure showed 98.52%, which summarizes the recall and precision, which could be considered to be more promising factors compared to sensitivity, specificity, and precision. The last statistical measure showed a weak level of agreement for this class. The third and healthy class showed accurate results in all networks where the data agreement value was also 0.97, showing an extraordinary level of agreement on the given data. The next class, mummification, showed 98.66% accuracy, 99.53% specificity, a 98.66% F1 score, a 99.53% precision value, and a 0.485 value for kappa. The accuracy for the mummification class was slightly better than that for the canker and dot classes. Other values such as specificity were lower than those for canker and dot, but with large differences. The precision value was higher than that of the canker class and lower than that of the dot class. To summarize both precision and specificity, the F1 score was used, which was nearer to that of the dot class and higher than that of the canker class. The kappa value is a decision-maker index, with a 0.48 value, which was less than that of the canker and dot classes, but also had weak agreement with the data reliability. The kappa value for the last class was 0.56 due to the one value for both precision and specificity, but 56 also lies in the weak agreement class. Lastly, the overall or mean results were evaluated, showing 98.50% accuracy, 99.60% specificity, a 98.75% F1 score, 98.75% precision, and a 0.9531 value for the kappa index.