UAV-based multispectral imaging helps understand crop Nitrogen Use Efficiency

Our recently accepted two papers by Wang J et al. (2025) and Wang F et al. (2025) investigated drone remote sensing data-driven models for crop nitrogen estimation. Both works utilize drone remote sensing technology to explore nitrogen dynamics in wheat, aiming to enhance nitrogen use efficiency (NUE) in agriculture.

The first paper examines the impact of soil mineral nitrogen on canopy structure and NUE, finding that low nitrogen levels cause significant shifts in canopy characteristics, specifically detecting this change through red-edge spectral indices from multispectral images. The study demonstrates high accuracy in predicting NUE during early growth stages using a multilayer perceptron model, highlighting UAV data’s potential to improve nitrogen management practices. The second paper focuses on assessing nitrogen content in different organs of winter wheat while also evaluating the performance of various image texture features and spectral bands for nitrogen monitoring. It establishes a workflow for mapping NUE, discovering that the effectiveness of specific features (vegetation indices, texture features) varied during different growth phases, and emphasizes the necessity of testing predictive models on independent datasets for broader applicability. Together, these two studies underscore the promising role of UAV-based multispectral imaging in advancing precision crop farming and optimizing NUE for sustainable crop production.