Comparative Evaluation of UAV Multispectral Sensors for Precision Agriculture: Spectral and Structural Consistency in Maize Canopies

Abstract

Precision agriculture increasingly relies on UAV-based multispectral sensors, but cross-platform data fusion remains limited by differences in spectral response, image geometry, and structural reconstruction accuracy. This challenge is particularly important in dense crop canopies, where vegetation indices and canopy height estimates may vary substantially between sensor platforms. This study compared the multi-lens DJI Phantom 4 Multispectral (P4M) and the dual-lens Sentera Double 4K in a maize hybrid trial (DKC 4596) in Debrecen, Hungary, using aerial surveys conducted at 35.5 m to assess DSM, DTM, NDVI, and NDRE outputs across 15 synchronized sampling plots processed in WebODM and RStudio. Agreement between platforms was evaluated using Bland–Altman analysis and Pearson correlation. The results showed a substantial systematic bias for NDVI, with Sentera producing 36% higher mean values than P4M and showing almost no linear association between platforms (r = 0.07). In contrast, NDRE demonstrated stronger and more manageable cross-platform agreement, with a significant positive correlation (r = 0.66, p = 0.007). Dense canopy closure reduced the reliability of absolute canopy height estimation for both platforms, but the P4M performed particularly poorly because of lens parallax and lower effective pixel density, while the higher-resolution Sentera imagery provided a more representative structural trend. Overall, NDRE is more suitable than NDVI for cross-platform vegetation index fusion between P4M and Sentera, while reliable structural assessment in tall, dense maize requires higher-resolution imagery, rigorous ground control, or complementary technologies such as LiDAR.