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Related Concept Videos

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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Key Elements for Plant Nutrition

Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the atmosphere, the...

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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

Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a

Charleston Dos Santos Lima1, Ana Júlia Teixeira Soares1, Bárbara da Silva Nogueira1

  • 1Department of Crop Science, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, RS, Brazil.

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

This study shows that vegetation indices from drone imagery can accurately predict maize field variables. The best indices depend on the growing season and crop stage, but a robust framework allows for high-accuracy predictions.

Keywords:
droneenvironmentphenotypingremote sensing

Related Experiment Videos

Last 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

Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Plant Science

Background:

  • Unmanned aerial vehicle (UAV)-based plant phenotyping faces challenges in correlating spectral data with field variables due to environmental influences and crop variability.
  • Maize phenological stages and growing seasons significantly impact the reliability of spectral information for crop assessment.
  • Accurate prediction of field variables is crucial for optimizing nitrogen management and crop yield.

Purpose of the Study:

  • To evaluate the interaction of nitrogen doses and environmental factors (phenological stages, growing seasons) on field variables and vegetation indices in maize.
  • To identify the most suitable vegetation indices for different evaluation environments.
  • To develop a methodology for predicting field variables using selected vegetation indices.

Main Methods:

  • Randomized complete block design with varying nitrogen (N) topdressing doses (0-400 kg ha⁻¹) across two growing seasons.
  • Data collection included agronomic variables and UAV-based image acquisition across five maize phenological stages.
  • Statistical analyses involved deviance analysis, variance components, principal component analysis (PCA), and multivariate linear modeling.

Main Results:

  • All vegetation indices were significantly affected by the interaction between N doses and evaluation environments (phenological stage and growing season).
  • A subset of indices (EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, OSAVI) demonstrated reliability and clustered based on environmental conditions.
  • The proposed methodology achieved high prediction accuracy (R² > 0.80) for most field variables, with exceptions for shoot biomass and 100-grain weight.

Conclusions:

  • Vegetation indices are sensitive to environmental variations, including phenological stage and growing season.
  • A framework utilizing fixed and random effects successfully predicts maize field variables with high accuracy.
  • The methodology offers a reliable approach for plant phenotyping using UAV-derived spectral data, aiding in crop management decisions.