# 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 last measure is to have confidence about the statistical analysis of the results, as statistical analysis is broadly used in many aspects of scientific work. Therefore, a statistical measure that gives confidence over confusion matrix values was calculated for evaluation. The kappa index gives the confidence over a certain range of confusion-matrix-based calculated values. If its value range lies in the range of 0-20, it promises that 0-4% of the data are reliable to use for prediction. If its index value lies in the range of 21-39, it promises a 4-15% data reliability. If it lies between 40 and 59, it promises 15-35% data reliability. If it lies between 60 and 79, it promises a 35% to 63% reliability. If it lies between 80 and 90, then it promises strong data reliability, and if it lies at more than 90, that means 82% to 100% reliability. It can be calculated as:  
\[Agreement=\frac{\big{(}\frac{cm_{1}*\,rm_{1}}{n}\big{)}+\big{(}\frac{cm_{2}xrm _{2}}{n}\big{)}}{n} \tag{15}\]  
The agreement type was calculated using \(cm_{1}\), \(cm_{2}\), \(rm_{1}\), and \(rm_{2}\), where \(cm_{1}\) represents Column 1 and \(cm_{2}\) represents Column 2. \(rm_{1}\) and \(rm_{2}\) represent Rows 1 and 2 of any two-class confusion matrix. This formulation is the general form of a two-class confusion matrix; in the case of our proposed methodology, there are five columns and rows that extend the formulation to up to five rows and columns. The total augmented data were later split into a 70/30 ratio for training and testing data, where the fine-tuned parameters for each of the five training models remained different and showed different results. The training and testing data number of instances became 2023 and 866, respectively. There were five different kinds of architectures applied to classify guava diseases using the augmented image data. The individual class testing data prediction results are discussed with their overall results. The evaluation measures accuracy, sensitivity, specificity, precision, recall, and the kappa index were also used as statistical measures. The results are shown in Table 7.  
The individual class and overall results for each model were evaluated. Several evaluation measures were used: accuracy, specificity, F1 score, precision, and kappa. The first model, AlexNet, showed 98% accuracy, 99% specificity, a 97.60% F1 score, 97.14% precision, and a 0.5296 value for kappa as the canker class prediction results of the testing data. Accuracy is a general measure over all positive and negative instances that are either wrongly or correctly predicted. The 98% accuracy of the canker class showed accurate predictions of positive and negative classes and mainly showed excellent and satisfactory results in TN predictions over TN and FP; specificity showed that true negatives were mostly predicted right among TN and FP. Precision showed TP over TP and FP, with a 97.14% value; this means that it had less accurate predictions. For a positive class to see the combined effect of TP and TN, the F1 score measure was used. The F1 score showed a 97% value, which summarizes both of the above measures. The kappa index showed a weak level of agreement for the canker class. The dot class showed 98.04% accuracy, 99.69% specificity, 98.52% F1 score, 99% precision, and a 0.53 kappa value. The dot class showed less accuracy than that of the canker class, but it needs to be discussed with another evaluation measure to analyze the predictions of positive and negative instances.