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April 24, 2026AgronomyOpen Access

Estimation of Leaf Water Content in Spring Wheat Based on UAV Multispectral Imagery

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Authors

JZJ. L. ZhuPZPinyuan ZhaoXAXiang Ao

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Overview

Field experiments develop irrigation-specific models for leaf water content in spring wheat, suggesting tailored approaches are essential.

Key Points

  • This study aims to create irrigation-specific models for estimating leaf water content in spring wheat using UAV multispectral imagery.
  • Conducted field experiments over two growing seasons (2023-2024) with three irrigation methods and five water treatments.
  • Collected multispectral data and in situ measurements at key growth stages.
  • Applied machine learning techniques, including Random Forest, Multiple Linear Regression, and Backpropagation Neural Network, for model development.
  • Spectral sensitivity to leaf water content varied significantly by irrigation method.
  • Random Forest model outperformed others with mean R2 values of 0.70, 0.74, and 0.62 for different methods and lower RMSE values.
  • Multiple Linear Regression had lower predictive accuracy, while Backpropagation Neural Network showed limited robustness.

Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/69eb0ac4553a5433e34b4b5ahttps://doi.org/10.3390/agronomy16090845
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