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Related Experiment Video

Updated: Jun 27, 2026

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

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Quantifying Canopy Closure Dynamics Using UAV Imagery and Semantic Segmentation in Rice Breeding Trials.

Yue Bao1,2, Fudeng Huang3, Weidong Lou2

  • 1College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.

Plants (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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This study introduces an efficient framework using drone imagery and deep learning to monitor rice canopy closure dynamics. This method aids in selecting high-yield rice varieties and optimizing cultivation practices.

Area of Science:

  • Agricultural Remote Sensing
  • Plant Physiology
  • Computational Biology

Background:

  • Canopy closure is crucial for rice development and yield.
  • Accurate monitoring of canopy closure is essential for rice breeding and cultivation.

Purpose of the Study:

  • To develop an efficient framework for quantifying rice canopy closure dynamics using UAV remote sensing and deep learning.
  • To analyze the relationship between canopy closure dynamics and grain yield in hybrid rice varieties.

Main Methods:

  • Utilized unmanned aerial vehicle (UAV) RGB images for 198 hybrid rice varieties.
  • Employed deep learning semantic segmentation models (DeepLabv3+, U-Net, PSPNet) for canopy feature extraction.
  • Applied the Gompertz model to characterize temporal canopy closure and derived key dynamic indicators.
Keywords:
Gompertz modelUAV remote sensingcanopy closure dynamicsdeep learningrice (Oryza sativa L.)semantic segmentation

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Last Updated: Jun 27, 2026

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08:47

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Main Results:

  • DeepLabv3+ achieved the highest performance (mIoU of 0.86) in canopy segmentation.
  • The Gompertz model accurately described canopy closure trajectories (average R² of 0.978).
  • Clustering analysis revealed distinct early-stage canopy development patterns, and canopy dynamics correlated with grain yield.

Conclusions:

  • The developed framework offers a scalable and objective method for quantifying rice canopy closure dynamics.
  • Findings support variety selection, cultivation optimization, and high-yield rice production.
  • The study highlights the importance of early-stage canopy development for rice yield.