Analysis of Vegetation Dynamics and Phenotypic Differentiation in Five Triticale (×<i>Triticosecale Wittm.</i>) Varieties Using UAV-Based Multispectral Indices

This study investigates the vegetation dynamics and phenotypic differentiation of five triticale (×<i>Triticosecale Wittm.</i>) varieties under the region-specific agroecological conditions of Southern Dobruja, Bulgaria, across two growing seasons (2024–2025), with the aim of evaluating how local climatic variability shapes vegetation index patterns. UAV-based multispectral imaging was employed throughout key phenological stages to obtain reflectance indices, including NDVI, SAVI, EVI2, and NIRI, which served as indicators of canopy development and physiological status. NDVI was used as the primary reference index, and a baseline value (NDVIbase), defined as the mean NDVI across all varieties on a given date, was applied to evaluate relative varietal deviations over time. Multiple linear regression analyses were performed to assess the relationship between NDVI and baseline biometric parameters for each variety, revealing that varieties 22/78 and 20/52 exhibited reflectance dynamics most closely aligned with expected developmental trends in 2025. In addition, the relationship between NDVI and meteorological variables was examined for the variety <i>Kolorit</i>, demonstrating that relative humidity exerted a pronounced influence on index variability. The findings highlight the sensitivity of triticale vegetation indices to both varietal characteristics and short-term climatic fluctuations. Overall, the study provides a methodological framework for integrating UAV-based multispectral data with meteorological information, emphasizing the importance of region-specific, time-resolved monitoring for improving precision agriculture practices, optimizing crop management, and supporting informed variety selection.