## 3 Results
### Comparison of the Results with Previous Studies  
To get a better understanding of the performance of the proposed methodology, it should be compared with results from previous studies. Different approaches have been used to identify yellow rust disease in winter wheat, based on vegetation indices (VI), machine learning, and deep learning. A yellow rust detection system was developed in [15], based on UAV imaging and different vegetation indices. The optimal ones for detecting yellow rust were RVI, NDVI, and OSAVI. The study also used a Random Forest classifier, which achieved precision, recall, and accuracy of 89.2%, 89.4%, and 89.3%, respectively. A similar approach was applied in [20], where UAV-obtained hyperspectral images were used to create different vegetation indices and texture features (TF). Three types of models were created--VI-based, TF-based, and VI-TF-based. The combined VI-TF model achieved the highest R\({}^{2}\) measures, ranging from 0.55 in the early stage of the infection to 0.88 in the later stages. NDVI was also used in [22] as an indicator for yellow rust disease. The proposed approach showed NDVI's moderate predictive capabilities with an R\({}^{2}\) of 0.514. Other studies presented results from the implementation of deep learning approaches. A deep convolutional neural network (DCNN) with multiple Inception-Resnet layers was applied in [19] on UAV-obtained hyperspectral images to identify yellow rust. The proposed model achieved an accuracy of 85%, which was validated using ground truth data. Similarly, in [26], a pyramid scene parsing network (PSPNet) semantic segmentation model was proposed for classifying yellow rust, healthy wheat, and bare soil. It used UAV-obtained RGB images as input and achieved an accuracy of 98% and a Kappa of 0.96. In [30], a UNetFormer2 model was proposed for the identification of yellow rust using multispectral images. The study achieved F1 scores of 0.784, 0.666, and 0.822 when using RGB, NDVI, and NIR images, respectively. The abovementioned results are summarized in Table 5.  
As mentioned earlier, most of the previous studies rely on multispectral data to achieve more or less accurate results. The methodology proposed in this study achieved better values of the measures used than the articles referenced did. The results achieved in [26] were similar to ours and were based on RGB imaging; however, our results also showed that the optimal UnetClassifier + ResNet34 model with histogram equalization filter applied has the potential for early identification of yellow rust disease, which might make it significantly better in practical situations. Furthermore, the area investigated in our study (270,027 m\({}^{2}\)) was significantly larger compared to that in [26] (2688 m\({}^{2}\)). In other words, our model demonstrated strong performance across a broader spatial extent,which makes it more representative and supports its applicability in large-scale agricultural monitoring scenarios. The abovementioned facts allows us to conclude that RGB imaging, which has been underestimated so far in favor of multispectral imaging, could provide a higher rate of yellow rust identification when combined with appropriate deep learning methodologies. Considering that RGB cameras are significantly cheaper in comparison to multispectral ones, this is expected to become the dominant approach for yellow rust identification in the near future. Although the UnetClassifier + ResNet34 model identified additional areas with subtle yellow discoloration that could potentially indicate early-stage infection, this observation remains hypothetical. The absence of multiple high-quality ground-captured images prevents definitive verification. Nevertheless, we note that all visible infections, including these subtle zones, were visually confirmed by a professional agronomist during in-field inspections, which were conducted concurrently with the UAV data collection. The locations of these observations were georeferenced and used to validate the spatial accuracy of the classification results.