Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

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.

Key Elements for Plant Nutrition

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Design, Synthesis of Arylhydrazide and Thiazolamine Derivatives Based on Benzodioxole Scaffold as Potential Antifungal Agents.

Journal of agricultural and food chemistry·2026

Same author

CRISPR/Cas12a and nanocomposite-based electrochemical/ colorimetric parallel dual-channel aptasensor for highly sensitive LDL detection.

Nanomedicine : nanotechnology, biology, and medicine·2026

Same author

Development and validation of an explainable model for Mycoplasma pneumoniae infection in children.

Scientific reports·2026

Same author

Novel aromatic amide Pan-PPAR agonists for dual amelioration of hepatic steatosis and islet dysfunction by promoting cholesterol efflux in the KKAy diabetic mice.

European journal of medicinal chemistry·2026

Same author

RCTUnet: a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction.

Journal of Zhejiang University. Science. B·2026

Same author

GAS-YOLO: a robust soybean seedling detection model trained on single-scene UAV data for complex field environments.

Plant methods·2026

Search research articles

Related Experiment Video

Updated: Dec 27, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.

Huilin Tao^1,2, Haikuan Feng^1,3,4, Liangji Xu²

¹Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China.

Sensors (Basel, Switzerland)

|March 4, 2020

Summary

Accurately estimating crop growth indicators like above-ground biomass (AGB) and leaf area index (LAI) is crucial for agriculture. Combining hyperspectral indices and red-edge parameters with the PLSR method significantly improves AGB and LAI estimation accuracy.

Keywords:

above-ground biomass leaf area index partial least squares regression red-edge parameters stepwise regression vegetation index

More Related Videos

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Related Experiment Videos

Last Updated: Dec 27, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Area of Science:

Agricultural remote sensing
Crop physiology
Geospatial analysis

Background:

Above-ground biomass (AGB) and leaf area index (LAI) are key indicators for crop growth assessment and agricultural management.
Current ground- and satellite-based monitoring methods face limitations in imaging, data processing, and spatial resolution.

Purpose of the Study:

To evaluate the efficacy of hyperspectral indices, red-edge parameters, and their combination for estimating AGB and LAI in winter wheat.
To compare the accuracy of different estimation methods for these crop growth parameters.

Main Methods:

Utilized a UAV-mounted hyperspectral sensor to acquire vegetation indices and red-edge parameters.
Employed stepwise regression (SWR) and partial least squares regression (PLSR) for AGB and LAI estimation.
Assessed the correlation between spectral parameters and crop growth indicators across various growth stages.

Main Results:

Most hyperspectral indices and red-edge parameters showed significant correlations with AGB and LAI.
Vegetation indices exhibited stronger correlations with AGB and LAI than red-edge parameters alone.
Combined use of vegetation indices and red-edge parameters yielded more accurate AGB and LAI estimations compared to using either alone.

Conclusions:

Combining vegetation indices with red-edge parameters enhances the accuracy of AGB and LAI estimation.
The PLSR method demonstrated superior performance over SWR for estimating AGB and LAI.
This integrated approach offers a more precise method for monitoring crop growth parameters.