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

Classifying Matter by Composition03:35

Classifying Matter by Composition

89.7K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
89.7K
Vision01:24

Vision

59.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
59.4K
Classifying Matter by State02:49

Classifying Matter by State

102.6K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
102.6K
Color Vision01:24

Color Vision

1.4K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.4K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

36.9K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
36.9K
Machines01:19

Machines

559
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
559

You might also read

Related Articles

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

Sort by
Same author

Evaluation of red CdTe and near infrared CdHgTe quantum dots by fluorescent imaging.

Journal of nanoscience and nanotechnology·2008
Same author

Exploring feasibility of multicolored CdTe quantum dots for in vitro and in vivo fluorescent imaging.

Journal of nanoscience and nanotechnology·2008
Same author

[Effects of Dahuang Zhechong Pill on expressions of tissue inhibitor of metalloproteinase-1 and plasminogen activator inhibitor-1 in rats with glomerulosclerosis].

Zhong xi yi jie he xue bao = Journal of Chinese integrative medicine·2008
Same author

[Construction and expression of a fusion protein containing extracellular domain of human Delta-like1 and Fc fragment of human IgG1 in the Pichia pastoris].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology·2008
Same author

[Preparation and characterization of the monoclonal antibodies to PTD-bcr/abl].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology·2008
Same author

Prostaglandin F2alpha induces the normoxic activation of the hypoxia-inducible factor-1 transcription factor in differentiating 3T3-L1 preadipocytes: Potential role in the regulation of adipogenesis.

Journal of cellular biochemistry·2008

Related Experiment Video

Updated: Jan 22, 2026

Application of Design Aspects in Uniaxial Loading Machine Development
05:23

Application of Design Aspects in Uniaxial Loading Machine Development

Published on: September 19, 2018

6.3K

Design of a tomato classifier based on machine vision.

Li Liu1, Zhengkun Li1, Yufei Lan1

  • 1College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China.

Plos One
|July 19, 2019
PubMed
Summary

This study introduces an automated tomato grading system using machine vision. The intelligent classifier accurately grades tomatoes by color, shape, and size, achieving 90.7% accuracy for real-time fruit assessment.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Related Experiment Videos

Last Updated: Jan 22, 2026

Application of Design Aspects in Uniaxial Loading Machine Development
05:23

Application of Design Aspects in Uniaxial Loading Machine Development

Published on: September 19, 2018

6.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Food Science

Background:

  • Traditional tomato grading relies on manual inspection, which is labor-intensive and subjective.
  • Automated grading systems are needed to improve efficiency and consistency in the fruit industry.
  • Machine vision offers a promising approach for objective quality assessment of agricultural products.

Purpose of the Study:

  • To design an automated, efficient, and intelligent tomato grading method.
  • To facilitate the graded selling of tomatoes based on objective quality parameters.
  • To develop a machine vision-based classifier for real-time tomato assessment.

Main Methods:

  • Utilized machine vision to analyze color images of tomatoes.
  • Selected key features: color (Hue value), shape (1D Fourier descriptor), and size (transverse diameter).
  • Developed separate classifiers for color, shape, and size, integrating them into a comprehensive grading system.

Main Results:

  • Investigated Hue value distributions to classify tomato maturity (mature, semi-mature, immature).
  • Employed 1D Fourier descriptor for contour irregularity and linear regression for size classification.
  • Achieved a mean grading accuracy of 90.7% for the automated system.

Conclusions:

  • The developed automated tomato grading method is efficient and intelligent.
  • The system effectively classifies tomatoes based on color, shape, and size.
  • The proposed machine vision approach enables automated, real-time grading with high accuracy.