# 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 mean or overall value was an actual representation of a model that produced good results; it was either about the accuracy, specificity specifically for TN, and precision for the TP value, and the F1 score represents the recall and precision. A kappa value of more than 90% is a strong agreement level, and the data reliability is also high when kappa is more than 90. Therefore, AlexNet overall showed satisfactory results. GoogLeNet showed testing results on the canker class as follows: 96.635% accuracy, 99.69% specificity, 97.81% F1 score, 99.01% precision, and 0.5285 kappa value. Accuracy showed promising results where, if examining the specificity value over TN values, it showed a value of 99.69%. Similarly, the precision value over the TP values showed 99%, and the F1 score over precision and recall showed a value of 97.81%, which showed more confidence than precision and specificity did. The last important measure is the kappa index, 0.5285, which showed weak promise as it was of only one class over other classes. The mean value showed the actual effect of the kappa stat. The other dot class showed values of 97.56% accuracy, 98.638% specificity, a 96.618% F1 score, 95.69% precision, and a 0.5269 kappa value. The accuracy value as compared to that of the canker class was lower. In the case of the dot class, where the specificity value was also slightly lower, its precision value was lower than that of the canker class. The F1 score based on precision and recall showed a slightly higher score in the canker class, where the last kappa Cohen index was similar and lied on weak agreement of data reliability. The mummification showed a 99.42% value of the accuracy, a 99.37% value of the specificity, a 97.29% value for the F1 score, a 98.18% precision value, and a 0.49 kappa value. For an accuracy value higher than those of the canker and dot classes, where the specificity value was lower than that of the canker, and slightly lower than that of the dot class, this means that some TN instances had variation in these cases. The F1 score showed a lower score than that of the dot class and higher than that of the canker class. This means the combined effect of TP and FP was more promising for mummification compared to that for the dot class. The last class of trust showed 99.47% accuracy, 99.114% specificity, a 98.172% value for the F1 score, 96.90% for precision, and 0.56 for the Cohen index. The accuracy value was higher than that of the canker, dot, and mummification classes. The F1 score showed a 98.17% value that was nearest the canker, mummification, and dot classes. Therefore, the accuracy value was the measure to analyze the test prediction results where other values such as the F1 score also mattered and made the results distinguishable. SqueezeNet testing showed results for the canker class of 97.596% accuracy, 99.392% specificity, a 97.838% F1 score, 98% precision, and a 0.52 kappa value. The accuracy value, a general assumption of model performance, had a lower value than that of specificity, precision, and the F1 score. The specificity value was much higher, which means that true negatives over the TN and FP had higher rates for the darker class. TP cases were also predicted with a 98% value over the FP and TP as the precision scores. The recall- and precision-based F1 score showed 97.83%, which is intermediate between the precision and specificity. The kappa value was 0.52 and lied on the weak agreement of the data. The second dot-class-based results showed 100% accuracy, 100% specificity, a 96.68% F1 score, 100% precision, and a 0.51 kappa value. Although the accuracy was good for this class, specificity showed a 100% result. The F1 score that covered recall and precision validity had a 96.68% score and 0.52 kappa value. The mummification class showed a predictivity of 98.214% accuracy, 98.91% specificity, a 97.56% F1-score, a 96.916% precision value, and 0.48 for the kappa index. The last class of rust showed predictivity measures of 91.53% accuracy, 100% specificity, a 95.58% F1 score, and 100% precision.