Comparative Assessment of Remote and Proximal NDVI Sensing for Predicting Wheat Agronomic Traits

Monitoring wheat traits across diverse environments requires reliable sensing tools that balance accuracy, cost, and scalability. This study compares the performance of proximal and UAV-derived NDVI sensing for predicting the key agronomic traits in winter wheat. The research was conducted at a long-term NPK field experiment on Haplic Chernozem soils in Rimski Šančevi, Serbia, using UAV multispectral imagery and a handheld proximal sensor to collect NDVI data across 400 micro-plots and six phenological stages. The UAV-derived NDVI achieved a higher mean value (0.71 vs. 0.60), lower coefficient of variation (29.2% vs. 33.0%), and stronger correlation with the POM readings (R2 = 0.92). For trait prediction, the UAV-based NDVI reached R2 values up to 0.95 for grain yield and 0.84 for plant height, outperforming the POM (maximum R2 = 0.94 and 0.83, respectively), and it showed superior temporal consistency (average R2 = 0.74 vs. 0.64). Although the POM performed comparably during mid-season under controlled conditions, its sensitivity to operator handling and limited spatial resolution reduced robustness in more variable field scenarios. A cost–benefit analysis revealed that the POM offers advantages in affordability, ease of use, and deployment in small-scale settings, while UAV systems are better suited for large-scale monitoring due to their higher spatial resolution and data richness. The findings highlight the importance of selecting sensing technologies based on biological context, operational goals, and resource constraints, and suggest that combining methods through stratified sampling may improve the efficiency and accuracy of crop monitoring in precision agriculture.