## 3 Results  
### Training, Testing, and Evaluation  
Out of the RGB image dataset experimentally obtained on 10 May 2024, six training and six testing images were selected, where the yellow rust can be observed using the naked eye. They were merged into bigger training and testing images, as shown in Figure 6.  
Next, the \"Histogram equalize\" filter was applied to them for better highlighting of the yellow rust disease. Thereafter, the ArcGIS Pro software was used to mark the reference training and testing data (Figure 7). To optimize this process, only areas that are obviously under the influence of yellow rust (marked in orange) or obviously without yellow rust (marked in green) were selected. Finally, according to step 4 from the methodology, an orthomosaic was created using all UAV-obtained data, and the \"Histogram Equalize\" filter was applied (Figure 8). Figure 6: Images, selected for training (**a**) and testing (**b**) purposes. The neural networks were trained using the following parameters: max epochs set to 30, batch size set to 8, validation set to 10%, chip size set to 256, monitoring metric set to \"Validation loss\", and data augmentation set to \"Default\". Furthermore, the model training was set up to stop when the model stops improving, and no changes in the backbone model were allowed. The model-specific arguments used in the training are summarized in Table 1.  
Figure 7: The created reference data for the training (**a**) and testing (**b**) images: orange—an area with yellow rust; green—an area without yellow rust. Five different neural networks were trained with and without the \"Histogram equalize\" filter applied, the accuracies of which are summarized in Table 2. It can be seen that when \begin{table}
\begin{tabular}{c c} \hline
**Model** & **Parameters** \\ \hline \hline \multirow{6}{*}{DeepLab v3} & class\_balancing=false \\  & mixup=false \\  & focal\_loss=false \\  & pointrend=false \\  & dice\_loss\_fraction=0 \\  & dice\_loss\_average=micro \\  & keep\_dilation=false \\ \hline \multirow{6}{*}{UnetClassifier} & class\_balancing=false \\  & mixup=false \\ \cline{1-1}  & focal\_loss=false \\ \cline{1-1}  & dice\_loss\_fraction=0 \\ \cline{1-1}  & dice\_loss\_average=micro \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model-specific parameters used for training. Figure 8: The created orthomosaic with the UAV path included (**a**) and the modified orthomosaic with the “Histogram equalize” filter applied (**b**). the original RGB data is used, the maximum accuracy reaches 0.847 with the DeepLabv3 ANN and the ResNet34 backbone model. If the \"Histogram equalize\" filter is applied, all trained models returned higher accuracy compared to their non-filtered versions, varying between 0.847 and 0.874. This indicates that the proposed filter offers very promising initial results. The highest accuracy was achieved by the Unit classifier with a ResNet34 backbone. Next, all trained models were applied to the testing data, again with and without \"Histogram equalize\" applied. To evaluate the resulting classification maps, the ArcGIS tool \"Accuracy assessment\" was used with 10,000 stratified random points. The obtained results are summarized in Table 3. It can be seen that all models, except the UNet ones without equalization, returned very good results. According to the testing results, the optimal models are the DeepLab v3 with ResNet34 and ResNet50, which achieved precision, recall, and F1 scores of 0.992. Their Cohen's Kappa coefficients are also almost identical, 0.981 and 0.980, respectively. Out of the histogram equalized models, the best metrics were achieved by the DeepLab v3 and UnetClassifier, both with ResNet34 backbones. Their precision, recall, F1 score, and Cohen's Kappa are almost identical, reaching 0.991, 0.991, 0.991, and 0.977, respectively.