Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m²) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R² of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m². Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.

To improve productivity, reduce production costs, and minimize the environmental impacts of agriculture, the advancement of nitrogen (N) fertilizer management methods is needed. The objective of this study is to compare the use of Unmanned Aerial Vehicle (UAV) multispectral imagery and PlanetScope satellite imagery, together with plant height, leaf area index (LAI), soil moisture, and field topographic metrics to predict the canopy nitrogen weight (g/m²) of wheat fields in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models, applied to either UAV imagery or satellite imagery, were evaluated for canopy nitrogen weight prediction. The top-performing UAV imagery-based validation model used SVR with seven selected variables (plant height, LAI, four VIs, and the NIR band) with an R² of 0.80 and an RMSE of 2.62 g/m². The best satellite imagery-based validation model was RF, which used 17 variables including plant height, LAI, the four PlanetScope bands, and 11 VIs, resulting in an R² of 0.92 and an RMSE of 1.75 g/m². The model information can be used to improve field nitrogen predictions for the effective management of N fertilizer.