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Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data.

Liang Han1,2,3, Guijun Yang1,4, Huayang Dai3

  • 1Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Plant Methods
|February 12, 2019
PubMed
Summary
This summary is machine-generated.

Estimating maize above-ground biomass (AGB) using unmanned aerial vehicle (UAV) remote sensing and machine learning offers a promising alternative to destructive sampling. This approach accurately predicts AGB by integrating structural and spectral data.

Keywords:
AGBBIOVPMachine learningMaizePlant heightUAV

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Area of Science:

  • Agricultural Science
  • Remote Sensing Technology
  • Data Science

Background:

  • Above-ground biomass (AGB) is crucial for assessing crop growth, agricultural management impacts, and carbon sequestration.
  • Traditional destructive sampling for AGB is labor-intensive, time-consuming, and impractical for large-scale, long-term studies.
  • Remote sensing offers an efficient, large-area alternative for monitoring crop biomass.

Purpose of the Study:

  • To develop and evaluate a machine learning model for estimating maize above-ground biomass (AGB) using unmanned aerial vehicle (UAV) remote sensing data.
  • To introduce an improved method for plant height extraction and a novel volumetric indicator (BIOVP) for biomass estimation.
  • To compare the performance of different machine learning algorithms and assess the influence of sampling methods on model accuracy.

Main Methods:

  • Utilized structural and spectral data from UAV remote sensing.
  • Applied recursive feature elimination to select six optimal predictor variables from an initial set of 14.
  • Evaluated four machine learning regression algorithms: multiple linear regression, support vector machine, artificial neural network, and random forest.
  • Developed an improved plant height extraction method and a volumetric indicator (BIOVP).

Main Results:

  • The random forest model demonstrated the best performance, achieving low errors and high explained variance for both training and testing datasets.
  • The volumetric indicator (BIOVP) was identified as the most influential predictor for AGB estimation across all evaluated machine learning models.
  • The proposed plant height extraction method significantly improved accuracy, increasing the explained variance ratio and reducing errors compared to traditional methods.

Conclusions:

  • The integration of machine learning with UAV remote sensing provides a robust and efficient method for estimating maize above-ground biomass (AGB).
  • Simultaneously considering structural and spectral information enhances the accuracy of estimating biophysical crop parameters.
  • This study highlights the potential of UAV-based remote sensing for precision agriculture and ecological monitoring.