Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

2.0K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.0K
Classification of Signals01:30

Classification of Signals

533
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
533
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.1K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.1K
Methods of Classification and Identification01:28

Methods of Classification and Identification

38
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
38
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

2.8K
Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
2.8K
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

14.0K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
14.0K

You might also read

Related Articles

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

Sort by
Same author

Detecting self-harm in social media using term weighting schemes based on the distance between words and personal pronouns.

Health information science and systems·2025
Same author

Semantic Segmentation in Large-Size Orthomosaics to Detect the Vegetation Area in <i>Opuntia</i> spp. Crop.

Journal of imaging·2024
Same author

Communication System Based on Magnetic Coils for Underwater Vehicles.

Sensors (Basel, Switzerland)·2022
Same author

Highly Discriminative Physiological Parameters for Thermal Pattern Classification.

Sensors (Basel, Switzerland)·2021

Related Experiment Video

Updated: Jul 21, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K

Varroa Destructor Classification Using Legendre-Fourier Moments with Different Color Spaces.

Alicia Noriega-Escamilla1, César J Camacho-Bello1, Rosa M Ortega-Mendoza1

  • 1Artificial Intelligence Laboratory, Universidad Politécnica de Tulancingo, Tulancingo 43629, Hidalgo, Mexico.

Journal of Imaging
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

Early detection of Varroa destructor mites in bees is crucial for hive health and food production. This study introduces a novel image analysis method using Legendre-Fourier moments for accurate mite identification, aiding bee preservation efforts.

Keywords:
Legendre–Fourier multichannel momentsVarroa destructorhoney bee

More Related Videos

Automated Charting of the Visual Space of Housefly Compound Eyes
08:34

Automated Charting of the Visual Space of Housefly Compound Eyes

Published on: March 31, 2022

1.9K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.7K

Related Experiment Videos

Last Updated: Jul 21, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K
Automated Charting of the Visual Space of Housefly Compound Eyes
08:34

Automated Charting of the Visual Space of Housefly Compound Eyes

Published on: March 31, 2022

1.9K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.7K

Area of Science:

  • Agricultural Entomology
  • Computer Vision
  • Biotechnology

Background:

  • Bees are vital for global food production through pollination.
  • The Varroa destructor mite is a major threat, causing viral infections and hive collapse.
  • Early disease detection is essential for bee colony health and preservation.

Purpose of the Study:

  • To develop an innovative method for early detection of Varroa destructor mites in honey bees.
  • To evaluate the efficiency of multichannel Legendre-Fourier moments for mite identification.
  • To compare the proposed method with existing deep learning techniques.

Main Methods:

  • Utilized multichannel Legendre-Fourier moments for image analysis of honey bees.
  • Employed a subdivided VarroaDataset to enhance feature extraction (color, shape of bee body parts).
  • Compared the proposed algorithm against DeepLabV3 and YOLOv5 for semantic segmentation and object detection.

Main Results:

  • The Legendre-Fourier moments approach demonstrated effective identification of Varroa destructor mites.
  • The method showed robustness with rotation and scale invariance, and noise resistance.
  • The proposed technique offers a promising alternative to current deep learning methods for mite detection.

Conclusions:

  • The developed method provides a novel and effective tool for early Varroa destructor mite detection.
  • This technology can significantly contribute to bee preservation strategies.
  • Protecting bee populations is critical for maintaining agricultural productivity and food security.