## 3 Results
### Practical Implications and Study Limitations  
The proposed methodology, which integrates UAV-based RGB imaging with deep learning techniques, demonstrates suitability for the early detection of yellow rust in wheat, particularly for small- and medium-scale farms. The level of detection accuracy achieved is sufficient to support informed decision-making by farmers and agronomists in the development of plant protection strategies. Only one high-resolution ground-level image was included in the study due to the poor quality and limited diagnostic value of the remaining ground photographs. However, yellow rust infection was reliably confirmed during field visits by an agronomist, who conducted visual inspections across the affected zones. These in-person assessments, although not fully documented through imagery, were georeferenced and used to establish the ground truth needed for model validation. The limited number of usable photographic samples restricts our ability to analyze early-stage infections in depth. Consequently, the identification of incipient symptoms by the model remains a promising but unverified capability, which we recognize as a limitation and plan to address in future studies through more comprehensive sampling. A key component of the methodology is the application of a \"histogram equalization\" filter to spatial data derived from RGB imagery. This approach enables more precise delineation of infected zones, thereby facilitating targeted fungicide application limited only to affected areas. Consequently, this selective treatment reduces unnecessary chemical use, minimizes associated costs for farmers, and mitigates environmental impact. In theory, the technical equipment employed is economically feasible for medium-sized farms and is user-friendly enough to be operated by individuals with medium computer literacy and UAV experience. Nevertheless, considering the computer knowledge and skills of most farmers, a more realistic approach would be to implement the methodology in a cloud-based service, which automates the process of testing UAV-obtained images using pre-trained models. Another possibility is to implement it in cooperatives or groups of smaller-scale producers involved in regional wheat field monitoring, thus making it accessible to a broader range of users in the agricultural sector. This way, it would be cheaper for them; however, it requires at least some of the farmers to have the necessary digital competencies. Despite its advantages, the UnetClassifier + ResNet34 model presents certain limitations. Interestingly, it was observed that the model is capable of identifying not only clearly visible yellow rust infections but also areas with less obvious symptoms, which may correspond to early or mild stages of infestation. Another limitation relates to the generalizability of the model. Its performance has not yet been validated across other crop species, disease types, or varying environmental conditions. Moreover, while the study highlights the practical potential of drone-based disease detection and precision fungicide application, the developed model has not been tested within an operational precision agriculture framework, such as that of the \"Zlatno Zarno\" agricultural cooperative. This represents a challenge for the large-scale adoption and integration of the approach by wheat producers in the region. It should be noted that the current study was conducted using a single wheat variety (Avenue), and as such, the model's adaptability to other cultivars with differing canopy structure, color, or disease expression patterns remains untested. Future research will need to assess the model's performance across multiple varieties to ensure broader applicability. Additionally, RGB image quality is highly dependent on environmental conditions during data acquisition. Overcast skies, low sun angles, or excessive cloud cover can lead to reduced contrast, diffuse shadows, and color inconsistency in the images, all of which may negatively affect model performance.