Estimating Crop Nitrogen Uptake from UAV-Based Imagery Using Machine Learning Techniques

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023–2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using ~520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R2 = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R2 = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R2 = 0.77, RMSE = 0.54% for MSI; R2 = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions.