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.

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

You might also read

Related Articles

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

Sort by

Same author

Improving nitrogen use efficiency in rice by estimating leaf nitrogen content with near-infrared spectroscopy and chemometric modeling.

Scientific reports·2025

Same author

Removal of Transgenes and Evaluation of Yield Penalties in Genome Edited Bacterial Blight Resistant Rice Varieties.

Plant biotechnology journal·2025

Same author

A neurotrophin functioning with a Toll regulates structural plasticity in a dopaminergic circuit.

eLife·2024

Same author

Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments.

Materials (Basel, Switzerland)·2024

Same author

TiNbN Hard Coating Deposited at Varied Substrate Temperature by Cathodic Arc: Tribological Performance under Simulated Cutting Conditions.

Materials (Basel, Switzerland)·2023

Same author

Intrahippocampal Inoculation of Aβ<sub>1-42</sub> Peptide in Rat as a Model of Alzheimer's Disease Identified MicroRNA-146a-5p as Blood Marker with Anti-Inflammatory Function in Astrocyte Cells.

Cells·2023

Same journal

Correction: Terletskaya et al. Soil-Climatic Drivers of Anatomical and Metabolic Plasticity in <i>Rheum tataricum</i> L.f. Across Arid Landscapes of Kazakhstan. <i>Plants</i> 2026, <i>15</i>, 1025.

Plants (Basel, Switzerland)·2026

Same journal

Correction: Damásio et al. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. <i>Plants</i> 2023, <i>12</i>, 4142.

Plants (Basel, Switzerland)·2026

Same journal

Apple Leaf Disease Detection Based on Improved YOLOv11 with DSSA Mechanism.

Plants (Basel, Switzerland)·2026

Same journal

New Pollen Morphological Perspectives into <i>Vernonia</i> (Compositae-Vernonieae) from Madagascar.

Plants (Basel, Switzerland)·2026

Same journal

The <i>DWARF27</i> Gene from Wintersweet (<i>Chimonanthus praecox</i>) Encodes an All-Trans/9-cis-β-Carotene Isomerase, Which Regulates Shoot Branching in <i>Arabidopsis</i>.

Plants (Basel, Switzerland)·2026

Same journal

Exogenous 6-Benzyladenine and Kinetin Restrict Rice Seedling Root Growth, and <i>Ag<sup>+</sup></i> Partially Alleviates the Inhibition in Association with Ethylene-Related Responses and Increased Lipid Peroxidation.

Plants (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 18, 2025

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

Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops.

Manuel G Forero¹, Claudia L Mambuscay¹, María F Monroy¹

¹Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia.

Plants (Basel, Switzerland)

|September 28, 2021

Summary

This summary is machine-generated.

This study compared image matching techniques for precision agriculture, finding the FAST feature detector with the BRISK descriptor to be most effective for identifying rice crops using visible and near-infrared images.

Keywords:

Brute Force matching FLANN matching feature descriptor feature detector image processing multispectral images

More Related Videos

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Published on: April 28, 2022

Related Experiment Videos

Last Updated: Oct 18, 2025

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

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Published on: April 28, 2022

Area of Science:

Agricultural Science
Computer Vision
Image Processing

Background:

Precision agriculture leverages machine vision and image processing for crop management.
Combining visible spectrum (VIS) and near-infrared (NIR) images aids crop identification.
Image matching between different sensors presents a significant challenge due to camera variations and distortions.

Purpose of the Study:

To compare the performance of various feature descriptors and detectors for image matching in rice crops.
To evaluate different spectral bands (RGB, CIE L*a*b*, NIR) for matching accuracy.
To identify the optimal algorithm for robust crop identification in precision agriculture.

Main Methods:

Acquisition of VIS and NIR images from different cameras across 20 rice crop scenes.
Extraction of RGB and CIE L*a*b* channels from VIS images for comparison with NIR images.
Implementation and evaluation of feature detection (FAST) and description (BRISK, SURF, SIFT, ORB, KAZE, AKAZE, BRIEF, FREAK) algorithms using Python and OpenCV.

Main Results:

The green channel consistently yielded the highest number of correct matches across all tested methods.
The combination of the FAST feature detector and the BRISK descriptor demonstrated superior performance.
This FAST-BRISK combination achieved the best balance between processing time and accuracy in matching VIS and NIR images.

Conclusions:

The FAST feature detector paired with the BRISK descriptor is highly effective for image matching in precision agriculture.
Utilizing the green channel from VIS images alongside NIR data significantly improves crop identification accuracy.
This optimized approach enhances the reliability of machine vision systems for agricultural applications.