Biostimulant Effects in Sugar Beet: Linking UAV Imaging, Leaf Ionomics, and Sugar Yield

The efficacy of protein hydrolysate (PH) biostimulants in mitigating abiotic stress and enhancing crop performance necessitates advanced monitoring techniques for validation. This study employed high-resolution unmanned aerial vehicle (UAV)-based multispectral imaging and ground spectroscopy to quantitatively assess the physiological and yield responses of sugar beet to a plant-derived PH biostimulant (Trainer®). A controlled field experiment compared foliar-applied treatment with an untreated control. UAV-derived vegetation indices revealed a significant 18% increase in NDVI within the treated plot, indicating superior canopy biomass and vigor. Spectral analysis demonstrated co-occurring increases in chlorophyll-sensitive indices (NDRE, CIre; +10%) and anthocyanin-related index (ARI1; +58%), alongside a reduced plant senescence index (PSRI). These findings suggest a dual effect of enhanced photosynthetic capacity and activation of secondary metabolism without associated stress. Ground-truthing via spectroradiometer confirmed significant improvements in chlorophyll content (SPAD, CI Green), nitrogen nutrition index (NNI), and antioxidant-related anthocyanin accumulation (ARI2). Ionomic analysis further confirmed enhanced nutrient assimilation, with treated plants exhibiting significantly elevated concentrations of key elements including N, K, Zn, and B. The physiological enhancements translated directly to agronomic gains, with treated plants yielding significantly higher root biomass (71.4 vs. 69.9 t ha⁻¹) and extractable sugar yield (10.5 vs. 9.2 t ha⁻¹). Crucially, the treatment effects were discernible via high-resolution UAV imagery but not with satellite-based monitoring, underscoring the critical importance of spatial resolution for detecting localized biostimulant effects. Our results demonstrate that PH biostimulant application elicits profound positive changes in sugar beet physiology, culminating in increased productivity, and establish UAV-based phenotyping as an indispensable tool for precision efficacy assessment in modern agriculture.