Drone-based Multispectral Imaging for Precision Monitoring of Crop Growth Variables

Drone-assisted crop growth monitoring has significantly boosted the demand for precision agriculture in recent years. Different vegetation spectral indices derived from drone-based multispectral images could be found more appropriate, as well as near-real-time monitoring tools over traditional methods and satellite remote sensing. The present study was conducted to estimate the leaf area index (LAI) and leaf nitrogen content (LNC) of wheat crops from drone-image-derived NDVI. Drone-based multispectral imaging of a wheat field with three wheat varieties (DBW-187, HD-3086, PBW-826) under eight nitrogen treatments (N0, N30, N60, N90, N120, N150, N180, N210) was completed at the flowering (90 DAS) and grain-filling stages (108 DAS), respectively. Multiple correlation analysis revealed that the squared Pearson’s correlation (R²) values of NDVI with LAI and LNC during the flowering stage were 0.78, 0.86, and 0.80 for DBW-187, HD-3086, and PBW-826, respectively, and improved to 0.89, 0.88, and 0.90 during the grain-filling stage. These results indicate a strong, positive relationship between NDVI, LAI, and LNC, which becomes stronger as the crop matures. Thus, drone remote sensing can effectively assess the biophysical variables of crops, potentially reducing the need for labor-intensive conventional methods of estimation. This study demonstrated that drone-assisted approaches can greatly enhance crop growth monitoring efficiency, offering a viable alternative to traditional methods.