###### Abstract  
Yellow rust (_Puccinia striiformis_) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach's potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model's application to other cereal diseases. yellow rust (_Puccinia striiformis_); identification; pixel-based classification; deep learning; RGB imaging; UnetClassifier
detection of yellow rust--especially in its early stages--is critical to minimizing yield loss and preserving grain quality, which highlights the need for practical and scalable monitoring solutions. Recent advancements in smart technologies have significantly improved the efficiency of agricultural monitoring, offering alternatives to traditional, labor-intensive field inspections. Numerous studies [9; 10; 11; 12; 13; 14; 15; 16; 17; 18] have demonstrated the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) in precision agriculture, enabling automated disease detection based on aerial imagery. Among the various imaging approaches, hyperspectral imaging has shown high potential for early detection of yellow rust due to its rich spectral information [19; 20; 21]. However, the widespread adoption of such technologies is hindered by the high cost of hyperspectral sensors, complex data processing requirements, and the need for specialized operator training. To address these limitations, alternative cost-effective methods have been proposed. For example, ref. [22] utilized low-cost cameras and the Normalized Difference Vegetation Index (NDVI) to assess the severity and distribution of infections, achieving an R-value of 71.7% and an R\({}^{2}\) of 51.4%. While promising, NDVI has limited specificity in distinguishing among various crop stress factors, often requiring supplementary field verification [23]. Multispectral cameras have been proposed as a more accessible solution [24], providing broader spectral coverage at a lower cost. Still, practical limitations such as restricted flight altitude, battery life, and the complexity of data handling remain challenges, particularly for non-specialist users in large-scale farming operations. One relatively underexplored yet promising alternative is the use of RGB imagery, which is both low-cost and readily available on most commercial UAVs. Despite its limited spectral depth, RGB data can be enhanced through appropriate processing and analyzed using deep learning algorithms for disease detection [25]. Recent studies have demonstrated that convolutional neural networks (CNNs) [26; 27; 28], as well as ensemble approaches combining traditional classifiers such as support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) [29], can achieve high classification accuracy using RGB inputs. Moreover, innovative architectures such as the CBAM-enhanced UNetFormer [30] show strong potential by integrating attention mechanisms and transformer-based decoding to improve feature representation in complex spatial data.