Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat

• Soil Nmin variations affect wheat yield, canopy structure, and nitrogen use efficiency. • UAV multispectral derived red-edge spectral indices are good predictors for NUE. • Machine learning models predicted NUE from UAV multispectral images. • Spectral indices and canopy height model outperformed texture features in predicting NUE. Drone remote sensing offers a powerful tool for monitoring vegetation and agricultural systems. However, its effectiveness in assessing the effect of soil mineral nitrogen ( N min ) on crop canopy traits remains inadequately explored. This study investigates the relationship between soil N min variability and canopy characteristics, grain yield, and nitrogen use efficiency (NUE), and explores the potential to predict NUE using drone multispectral images. Multispectral data were collected across growth stages over two growing seasons. The analysis revealed that soil N min significantly affected canopy structure, with low N min inducing a ’blue shift’ of the red-edge spectral position. The multilayer perceptron regression model predicted NUE with high accuracy (R 2 > 0.7) in early growth stages, identifying red-edge spectral indices and canopy height as key predictors. Texture features did not play a significant role in the models for predicting NUE, which remains to be further understood in future research. These findings highlight the capability of UAV remote sensing data, especially the red-edge spectral features, to capture the effects of soil N min on canopy traits. This study provides a proof-of-concept for mapping NUE using UAV images, with the final goal of improving crop nitrogen management and fertilizer use efficiency in agriculture.