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Updated: May 10, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features.

Yuchen Wang1, Jianliang Wang2,3, Jiayue Li2,3

  • 1College of Hydraaulic Science and Engineering, Yangzhou University, Yangzhou 225009, China.

Plants (Basel, Switzerland)
|April 23, 2025
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Summary

Accurate estimation of maize leaf water content (LWC) is vital for crop management. This study uses UAV multispectral imagery and a Random Forest Regression model to precisely estimate LWC across growth stages.

Keywords:
UAVleaf water contentmachine learningmultispectral imagewheat

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Data Science

Background:

  • Leaf water content (LWC) is a critical indicator of maize moisture status, directly impacting crop yield and growth.
  • Precise LWC estimation is fundamental for effective water resource management and the implementation of precision agriculture strategies.
  • Current methods for LWC assessment require improvement in accuracy and efficiency for large-scale agricultural monitoring.

Purpose of the Study:

  • To develop and validate a high-precision method for estimating maize LWC using UAV-based multispectral imagery.
  • To evaluate the performance of a Random Forest Regression (RFR) model in estimating LWC across different maize growth stages.
  • To compare the efficacy of the RFR model against traditional regression techniques and assess the impact of optimization on prediction accuracy.

Main Methods:

  • Acquisition of UAV-based multispectral imagery of maize crops.
  • Extraction of relevant features including vegetation indices, image coverage, and texture features.
  • Integration of extracted features with ground-truth LWC data for model training and validation.
  • Application of Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Ridge Regression (RR) models.
  • Optimization of the RFR model using Particle Swarm Optimization (PSO).

Main Results:

  • The RFR model demonstrated optimal performance during the seedling stage (RRMSE: 2.99%) compared to the tasseling stage (RRMSE: 4.13%).
  • RFR consistently outperformed MLR and RR models in LWC estimation accuracy across all growth stages, with lower errors on both training and testing datasets.
  • Particle Swarm Optimization (PSO) significantly enhanced RFR model accuracy, reducing training dataset RRMSE from 1.46% to 1.19%.

Conclusions:

  • UAV-based multispectral imagery combined with RFR offers a highly accurate and effective approach for estimating maize LWC throughout its growth cycle.
  • The RFR model provides superior performance over MLR and RR, highlighting its suitability for complex agricultural data analysis.
  • This methodology supports improved crop water management, precision agriculture practices, and offers a transferable framework for estimating water content in other crops.