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Related Concept Videos

Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

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Related Experiment Video

Updated: Jun 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Monitoring plant moisture content and optimizing irrigation prescriptions based on UAV multimodal data.

Wanna Fu1,2, Xinyue Hou1, Dawei Wang1

  • 1Heilongjiang Provincial Hydraulic Research Institute, Harbin, China.

Frontiers in Plant Science
|June 1, 2026
PubMed
Summary

This study developed a high-precision winter wheat Plant Moisture Content (PMC) prediction model using multi-modal UAV data and machine learning. Stage-specific irrigation scheduling based on PMC thresholds improved water use efficiency in precision agriculture.

Keywords:
modified evapotranspirationplant moisture contentprecision irrigationunmanned aerial vehiclewater use efficiency

Related Experiment Videos

Last Updated: Jun 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

  • Precision Agriculture
  • Remote Sensing
  • Crop Monitoring

Background:

  • Smart agriculture advancements necessitate precise crop moisture monitoring.
  • Unmanned Aerial Vehicles (UAVs) offer multi-modal remote sensing capabilities for crop management.
  • Challenges persist in developing accurate field-scale Plant Moisture Content (PMC) prediction models for irrigation.

Purpose of the Study:

  • To develop high-precision PMC prediction models for winter wheat at different growth stages.
  • To integrate PMC monitoring with water use efficiency (WUE) for field-scale irrigation scheduling.
  • To evaluate the effectiveness of multi-modal UAV data and machine learning algorithms for crop water status assessment.

Main Methods:

  • Field experiments were conducted on winter wheat, measuring PMC and WUE.
  • Leaf Area Index (LAI) was inverted from UAV-derived Vegetation Indices (VIs).
  • Crop Height was extracted from UAV point cloud data.
  • An improved evapotranspiration (ET) model was developed using multispectral, thermal infrared, plant height, and LAI data.
  • PMC prediction models were established using VIs, temperature indices (TIs), and ET with machine learning algorithms (RFR, BPNN, PLSR, SVR).

Main Results:

  • Evapotranspiration (ET) showed the highest correlation with PMC during jointing and heading stages (|r| ≥ 0.639).
  • The Random Forest Regression (RFR) model with multi-modal inputs (VIS+TIs+ET) achieved the highest predictive accuracy (R²=0.900, nRMSE=2.688%) during the grain-filling stage.
  • Optimal WUE varied by growth stage and irrigation treatment, with peak values achieved under specific PMC thresholds.

Conclusions:

  • Integrating multi-modal UAV data, machine learning, and an improved ET model enables high-precision PMC monitoring.
  • Stage-specific irrigation scheduling based on PMC thresholds can significantly enhance overall water use efficiency.
  • The developed framework supports data-driven irrigation scheduling in precision agriculture.