## 3 Results
### Training, Testing, and Evaluation
There is an obvious contradiction between the training and testing results, as the accuracy metrics of the histogram equalized models were better compared to their unequalized twins, while in the testing phase, it is the opposite for some of the models. This might be explained by the difference in the data distribution in the images selected for testing \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model + Backbone**} & **Accuracy with** & **Accuracy Without** \\  & **“Histogram Equalize”** & **“Histogram Equalize”** \\ \hline DeepLab v3 + ResNet18 & 0.818 & 0.857 \\ \hline DeepLab v3 + ResNet34 & 0.847 & 0.864 \\ \hline DeepLab v3 + ResNet50 & 0.838 & 0.873 \\ \hline UnetClassifier + ResNet18 & 0.800 & 0.847 \\ \hline UnetClassifier + ResNet34 & 0.762 & 0.874 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results from the training data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Model + Backbone**} & **Precision** & **Recall** & **F1 score** & **Cohen’s Kappa** \\ \hline \multicolumn{5}{c}{**Without “Histogram equalize”**} \\ \hline DeepLab v3 + ResNet18 & 0.990 & 0.990 & 0.990 & 0.975 \\ \hline DeepLab v3 + ResNet34 & 0.992 & 0.992 & 0.992 & 0.981 \\ \hline DeepLab v3 + ResNet50 & 0.992 & 0.992 & 0.992 & 0.980 \\ \hline UnetClassifier + ResNet18 & 0.964 & 0.959 & 0.960 & 0.897 \\ \hline UnetClassifier + ResNet34 & 0.950 & 0.950 & 0.950 & 0.877 \\ \hline \multicolumn{5}{c}{**With “Histogram equalize”**} \\ \hline DeepLab v3 + ResNet18 & 0.985 & 0.984 & 0.984 & 0.961 \\ \hline DeepLab v3 + ResNet34 & 0.991 & 0.991 & 0.991 & 0.977 \\ \hline DeepLab v3 + ResNet50 & 0.987 & 0.987 & 0.987 & 0.968 \\ \hline UnetClassifier + ResNet18 & 0.984 & 0.983 & 0.983 & 0.958 \\ \hline UnetClassifier + ResNet34 & 0.991 & 0.990 & 0.990 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results from the testing data. and training (Figure 7) and the corresponding classified areas. Therefore, to get a better understanding of the models' performance, additional analysis is required. The following models have been applied to the created orthomosaics:  
* DeepLab v3 + ResNet34 without histogram equalization (Figure 9);
* DeepLab v3 + ResNet50 without histogram equalization (Figure 10);
* DeepLab v3 + ResNet34 with histogram equalization (Figure 11);
* UnetClassifier + ResNet34 with histogram equalization (Figure 12). Figure 11: Orthomosaic classifications using the DeepLab v3 + ResNet34 model with histogram equalization (the areas marked in red were classified as yellow rust-infested). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Yellow Rust**} & \multicolumn{2}{c}{**Healthy**} \\ \cline{2-5}  & **Area, m\({}^{2}\)** & **Relative Area, \%** & **Area, m\({}^{2}\)** & **Relative Area, \%** \\ \hline DeepLab v3 + ResNet34 without histogram equalization & 25,851 & 9.6\% & 244,176 & 90.4\% \\ \hline DeepLab v3 + ResNet50 without histogram equalization & 22,453 & 8.3\% & 247,574 & 91.7\% \\ \hline DeepLab v3 + ResNet34 with histogram equalization & 40,895 & 15.2\% & 229,132 & 84.8\% \\ \hline UnetClassifier + ResNet34 with histogram equalization & 55,244 & 20.5\% & 214,783 & 79.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the classification results by the four models. Figure 12: Orthomosaic classifications using the UnetClassifier + ResNet34 model with histogram equalization (the areas marked in red were classified as yellow rust-infested). Figure 14: Close-up samples from the orthomosaic with red outlines of the yellow rust areas, classified using the unequalized DeepLab v3 + ResNet34 (**a**), unequalized DeepLab v3 + ResNet50 (**b**), equalized DeepLab v3 + ResNet34 (**c**), and equalized UnetClassifier + ResNet34 (**d**) models.