## 2 Materials and Methods
### Methodology of the Study  
The study's methodology consists of eight steps and is summarized in Figure 4.  
**Step 1. ** The first step includes data acquisition, using a UAV. In this study, visible spectrum data is assumed. **Step 2. ** The second step is aimed at the preparation of training data. It includes three subphases. The first one includes the selection of appropriate images from the acquired data, in which the yellow rust can be clearly observed. The selected images are merged into a bigger image using any image editing software. In the current study, we have used Corel Photo-Paint, version 24.0.0.301. In the next subphase, the \"histogram equalize\" filter is applied. In the current study, it is done using Corel Photo-Paint's \"Equalize\" tool. This filter allows for highlighting of the yellow rust, as can be observed in Figure 5.  
In the third subphase, reference data is created in the ArcGIS Pro 3.4.0 software, developed by Esri Inc. (Redlands, CA 92373, USA). This is achieved by applying the \"Label Objects for Deep Learning\" function of the tool. Two classes are defined:  
Yellow rust--indicates there is yellow rust disease in the corresponding area;  
Healthy--indicates no yellow rust has been identified. Thereafter, numerous polygons are created on the training image, which are to be used for training the artificial neural networks. Figure 4: A summary of the study methodology. **Step 3. ** This step is identical to Step 2; however, the goal is to create independent data for testing the trained models. To achieve this, the same procedure is applied as in Step 2; however, different images are selected and combined, which were not used in the training phase. Thereafter, the \"histogram equalize\" filter is applied, and regions of interest are selected for verification purposes. **Step 4. ** The fourth step of the methodology is aimed at creating an orthomosaic, which combines all UAV images into a single high-quality map. The initial training samples are drafted using selected UAV images with central projection, which may contain geometric distortion. To ensure spatial consistency and accurate downstream analysis, a high-resolution orthorectified image mosaic (DOM) is generated from the full UAV dataset. This orthomosaic serves as the primary reference for classification and validation tasks in the later stages of the methodology. This is implemented in ArcGIS Pro by creating a new workspace with the images, followed by adjusting them, creating a digital surface model, and the follow-up orthomosaic. Next, the created ortho is exported as an image, the \"Histogram equalize\" filter is applied to it, and it is imported back into ArcGIS Pro for further analysis. **Step 5. ** The methodology continues with training different ANN models. In the current study, the following combinations are adopted:  
* DeepLab v3 convolutional neural network with backbones ResNet18, ResNet34, and ResNet50. DeepLab is a deep neural network used for semantic segmentation. Previous studies [35; 36] have shown that this architecture returns very good results when used with UAV-obtained RGB images and agricultural tasks. * U-Net classifier architecture with backbones ResNet18 and ResNet34. U-Net was initially developed for application in biomedical image segmentation; however, different studies [37] have shown it to be appropriate for UAV-obtained data. Figure 5: Examples of a ground-based image of yellow rust (**a**), the same image with histogram equalization applied (**b**), a UAV-obtained image with yellow rust (**c**), and the same image with histogram equalization applied (**d**). All models were trained on an NVIDIA GeForce MX350 graphical processing unit, and the process took less than 12 h each. **Step 6. ** In the next step of the methodology, the trained models are applied to the testing data to evaluate their performance. The training is implemented in the ArcGIS Pro software, using the \"Classify Pixels Using Deep Learning\" tool.