## 2 Materials and Methods
### Spectral Vegetation Indices  
Remote sensing techniques have demonstrated their effectiveness across a range of agricultural applications, such as crop classification, yield prediction, and monitoring plant health and nutrient deficiencies. There is a growing emphasis on precision farming and smart agricultural resource management systems aimed at enhancing productivity, profitability, and environmental sustainability. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Flight height & 50 m a.s.l. \\ Overlap front. and lat & 70\% \\ Flight speed & 10 m s\({}^{-1}\) \\ Camera angle & 90\({}^{\circ}\) \\ FOV & 62.7\({}^{\circ}\) \\ Flight path & 0\({}^{\circ}\) to the North \\ GSD & \(\approx\)2.6 cm \\ GNSS mode & RTK \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flight and sensor parameters. This technology plays a critical role in sustainable farming practices, allowing farmers to assess soil and crop health at key growth stages. Early evaluations help optimize fertilizer application, while later assessments support health evaluations and yield predictions. Remote sensors deliver timely data on biophysical indicators and their spatial variations, enabling variable rate technology that customizes fertilizer usage according to specific conditions. Among the essential nutrients, nitrogen is particularly crucial for crop growth, as it affects chlorophyll content and photosynthesis. However, excessive nitrogen can lead to runoff and aquatic pollution, while inadequate levels can diminish crop yields and result in financial losses. To tackle these challenges, accurately assessing nitrogen levels in fields is essential, particularly during the early stages of crop growth. Remote sensing of vegetation utilizes passive sensors to capture electromagnetic reflectance from plant canopies, with reflectance characteristics varying based on plant type and water content. Healthy plants typically exhibit high reflectance, whereas dry plants display lower reflectance values. Vegetation indices obtained from reflectance data can be used to evaluate various plant characteristics, such as water content and stress levels, thereby enhancing our understanding of plant dynamics. However, interpreting remote sensing data can be complex and is often constrained by the reliance on a limited number of spectral bands. Despite these difficulties, researchers are developing advanced algorithms and techniques for assessing plant health, showing that even basic vegetation index algorithms can be effective tools for monitoring agricultural health [57]. Remote sensing methods have proven to be valuable for assessing nitrogen status and crop health. Research shows that nitrogen deficiency leads to a decrease in leaf chlorophyll content, which increases transmittance at visible wavelengths and alters the reflectance of crop leaves. These alterations enable the estimation of chlorophyll concentration and nitrogen variability. As a result, the optical indices employed to estimate chlorophyll levels are affected not only by these additional factors but also by the reflectance of the soil. Recent studies have increasingly concentrated on the connection between the optical properties of vegetation and the concentrations of photosynthetic pigments, especially chlorophyll-a, chlorophyll-b, and carotenoids. These pigments display unique spectral characteristics, with specific absorption features at different wavelengths, which allows remote sensing techniques to assess their effects on vegetation reflectance [23]. This has resulted in the creation of numerous approaches, both empirical and model-based, for estimating chlorophyll content at both the leaf and canopy levels [58; 59]. Research has largely focused on employing optical indices to enhance sensitivity to chlorophyll content while minimizing variability from other influences. This has involved assessing reflectance in narrow spectral bands, utilizing band ratios, and analyzing the characteristics of derivative spectra.