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Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Multispectral UAV imaging and machine learning for estimating wheat nitrogen nutrition index.

Chao Zhang1,2, Xinyi Lu2, Haolei Zhang2

  • 1Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, China.

Frontiers in Plant Science
|December 31, 2025
PubMed
Summary

This study used UAV multispectral imagery to develop a model for estimating wheat nitrogen nutrition index (NNI). The model accurately predicts NNI, aiding in optimized nitrogen fertilizer application and planting density for spring wheat.

Keywords:
multispectralnitrogen application levelnitrogen nutrition indexplanting densityspring wheatvegetation indices

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Crop Physiology

Background:

  • Accurate monitoring of wheat nitrogen nutrition is vital for optimizing fertilizer application and ensuring crop yield.
  • Existing methods for assessing nitrogen status can be labor-intensive and time-consuming.
  • Developing efficient, non-destructive techniques for nitrogen nutrition index (NNI) estimation is crucial for precision agriculture.

Purpose of the Study:

  • To estimate the wheat nitrogen nutrition index (NNI) using UAV-based multispectral imagery.
  • To investigate the impact of varying planting densities and nitrogen application rates on NNI.
  • To construct a robust model for NNI estimation in spring wheat.

Main Methods:

  • UAVs captured multispectral canopy imagery of wheat at key growth stages.
  • Vegetation indices were selected based on correlation and feature importance analysis.
  • A Bayesian optimized random forest model was developed to estimate NNI.

Main Results:

  • Several vegetation indices (DVI, MDD, NGI, MEVI, NDVI, EVI, ENDVI) showed high robustness for NNI estimation.
  • The optimal NNI estimation model achieved R² of 0.785 and RMSE of 0.137 under specific nitrogen application rates (N2).
  • The best NNI model at a planting density of P1 (1 million plants/hm²) yielded R² of 0.716 and RMSE of 0.158.

Conclusions:

  • The study successfully developed an accurate NNI estimation model using UAV multispectral data.
  • Findings provide insights into how planting density and nitrogen levels influence wheat NNI.
  • The model serves as a valuable tool for assessing wheat growth and guiding optimal management practices.