## 4 Conclusions  
Yellow rust (_Puccinia striiformis_) is a major wheat disease causing significant yield losses if not detected early. In regions like Southern Dobrudja, where wheat is a key crop, timely and accurate detection is essential for effective disease management within precision agriculture frameworks.  
Unlike most previous studies that rely on multispectral data, in this one, a methodology is proposed for the identification of yellow rust disease using UAV-obtained RGB imaging. Different deep learning models were trained and compared to obtain the optimal-performing one. An important aspect of the proposed approach is the application of the \"histogram equalize\" filter on all RGB images, which makes the yellow rust easily discoverable even with the naked eye. The UnetClassifier with ResNet34 backbone showed the best performance and achieved an accuracy, precision, recall, F1 score, and Cohen's Kappa of 99.1%, 99.0%, 99.0%, 87.4%, and 0.977, respectively.  
The model's performance was also evaluated manually using the generated orthomosaic. It was established that the UnetClassifier + ResNet34 model identifies not only areas that are obviously under the impact of yellow rust, but also other areas that might be at an earlier stage of infestation. While the available field data makes us optimistic that this is the case, it is not enough to fully prove or reject this hypothesis, which leaves it as an important topic for future research. A more comprehensive ground-truth validation campaign would be necessary to determine the model's actual sensitivity in detecting subclinical or incipient infections and to assess its reliability in such contexts.  
Future research will also aim to enhance the accuracy of disease detection in areas with low infection intensity. This will be achieved by conducting exploratory UAV flights at varying altitudes to determine the optimal image acquisition height for RGB-based identification of wheat rust. In addition, efforts will be directed toward expanding the model's capabilities to detect a broader spectrum of plant diseases, not only in wheat but also in other cereal crops.  
Conceptualization, A.Z.A. and B.I.E.; methodology, B.I.E.; software, B.I.E.; validation, A.Z.A., B.I.E. and A.I.A.; formal analysis, A.Z.A. and B.I.E.; investigation, A.Z.A.; resources, P.D.N.; data curation, B.I.E.; writing--original draft preparation, A.Z.A. and B.I.E.; writing--review and editing, A.I.A.; visualization, B.I.E.; supervision, A.Z.A.; project administration, A.Z.A.; funding acquisition, A.Z.A. All authors have read and agreed to the published version of the manuscript.  
This study is financed by the European Union--NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project N\({}^{\circ}\) BG-RRP-2.013-0001.  
Not applicable.  
The datasets used in this study are published under the CC BY 4.0 license and can be found at [[https://doi.org/10.6084/m9.figshare.29435546](https://doi.org/10.6084/m9.figshare.29435546)]([https://doi.org/10.6084/m9.figshare.29435546](https://doi.org/10.6084/m9.figshare.29435546)) (accessed on 30 June 2025).  
The authors declare no conflicts of interest.