This study investigates the potential for early yield prediction in nine winter wheat (Triticum aestivum) varieties using multispectral data acquired at different growth stages. The data were collected using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor including a near-infrared (NIR) band and an RGB camera. An assessment of data acquisition and processing accuracy was conducted. The average ground sampling distance (GSD) was 0.42 cm, compared to the pre-flight planned value of 0.54 cm/pixel. The total processing error ranged between 0.34% and 0.45% of a pixel. Five vegetation indices were analyzed, including three NIR-based indices (NDVI, EVI2, and SAVI) and two RGB-based indices (MPRI and MGVRI). The strongest relationships between yield and NIR-based indices were observed on 26 April (spindle phase), with coefficients of determination (R2) ranging from 0.98 to 0.99, while the weakest relationships occurred in late March (R2 = 0.75–0.80). In contrast, RGB-based indices showed the strongest correlation in early December (R2 = 0.99) and the weakest on 26 April (R2 = 0.38–0.40). Regression models for yield prediction were developed based on both groups of vegetation indices. The results demonstrate that the predictive capability of vegetation indices varies significantly across growth stages, highlighting the importance of temporal data selection for accurate yield estimation.
Atanasov et al. (Wed,) studied this question.