# 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
For individual cases or data-based testing analysis, the confusion matrices were designed and evaluated, and they are shown in Table 8.  
The confusion matrices of all architectures showed that the rust class was the most distinguishable among other guava diseases. If we discuss the AlexNet model results, four wrong cases were predicted as wrong in the rust and mummification classes, four wrong predictions were found for the dot class, where the wrong predictions lied on canker and rust. There were three wrong predictions for mummification, in the class of canker, and there were two wrong predictions in the rust class; these wrong predictions were two for dot. The second GoogLeNet architecture made 201 correct predictions in the canker class, and seven wrong predictions lied in the dot, mummification, and rust class, while no prediction lied in the healthy class. The five wrong predictions for the dot class were two predicted as mummification and three was rust, whereas 200 were predicted as right. This makes it one case less accurate than AlexNet, as that made four wrong predictions in the dot class. Mummification was predicted as nine wrong classes in the canker, dot, mummification, and rust categories, where two-hundred sixteen cases were rightly predicted. In rust, 188 cases were rightly predicted. One was predicted wrongly in the dot class. It was highly more efficient than AlexNet was in this category, as that predicted two wrong cases in the dot class, and GoogLeNet predicted only one wrong. The SqueezeNet architecture model made five wrong predictions, with two-hundred and three correct predictions of the canker class. Figure 4: Guava disease classification result visualization. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{l}{**Networks**} & \multicolumn{4}{c}{**AlexNet**} & \multicolumn{4}{c}{**GoogLeNet**} & \multicolumn{4}{c}{**SqueezeNet**} & \multicolumn{4}{c}{**ResNet-50**} & \multicolumn{4}{c}{**ResNet-101**} \\ \hline
**Classes** & **C** & **D** & **H** & **M** & **R** & **C** & **D** & **H** & **M** & **R** & **C** & **D** & **H** & **M** & **R** & **C** & **D** & **H** & **M** & **R** & **C** & **D** & **H** & **M** & **R** \\ \hline
**C** & **204** & **0** & **0** & **3** & **1** & **201** & **3** & **0** & **2** & **2** & **203** & **0** & **0** & **5** & **0** & **207** & **1** & **0** & **0** & **0** & **0** & **207** & **0** & **0** & **0** & **1** \\ \hline
**D** & **3** & **201** & **0** & **0** & **1** & **0** & **200** & **0** & **2** & **3** & **0** & **205** & **0** & **0** & **0** & **0** & **205** & **0** & **0** & **0** & **2** & **201** & **0** & **0** & **2** \\ \hline
**H** & **0** & **0** & **40** & **0** & **0** & **0** & **0** & **40** & **0** & **0** & **0** & **0** & **40** & **0** & **0** & **0** & **0** & **40** & **0** & **0** & **0** & **0** & **40** & **0** & **0** & **0** \\ \hline
**M** & **3** & **0** & **0** & **221** & **0** & **2** & **5** & **0** & **216** & **1** & **1** & **3** & **0** & **220** & **0** & **2** & **1** & **0** & **221** & **0** & **3** & **1** & **0** & **220** & **0** \\ \hline
**R** & **0** & **2** & **0** & **0** & **187** & **0** & **1** & **0** & **0** & **188** & **3** & **11** & **0** & **2** & **173** & **0** & **0** & **0** & **0** & **189** & **3** & **0** & **0** & **0** & **186** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix obtained using several state-of-the-art networks. The five wrong predictions were in the mummification class. In the dot class, there was no wrong prediction, and all 205 test instances were rightly predicted. In mummification, there were four wrongly predicted cases and two-hundred and twenty rightly predicted cases. Most were predicted in the dot class with three instances, with one predicted in the canker class. Rust had 16 wrong cases, and most were in the dot class--11 out of 16. ResNet-50 was similar to ResNet-101, where the difference was mainly of several layers and its parameters.