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

Methods of Classification and Identification

Methods of Classification and Identification

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...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Fruit Development, Structure, and Function

Fruit Development, Structure, and Function

Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

Bibliometric analysis of research trends on nanotechnology applications in atherosclerosis.

Discover nano·2026

Same author

Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.

Journal of thoracic disease·2026

Same author

Post-liver transplantation delirium: Pathogenesis, risk factors, clinical management, and future directions.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2026

Same author

Spin-Polarized Luminescence Modulated by Magnetic Coupling in Glass-Embedded Eu<sup>2+</sup>-Doped Lead-Free Perovskite Nanocrystals.

ACS nano·2026

Same author

Ecosystem simulation: the software to platform leap.

Scientific reports·2026

Same author

Hydrogen crossover raises serious concerns on proton exchange membrane water electrolyzer.

Innovation (Cambridge (Mass.))·2026

Search research articles

Related Experiment Video

Updated: Dec 19, 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

Classification of fruits using computer vision and a multiclass support vector machine.

Yudong Zhang¹, Lenan Wu

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China. zhangyudongnuaa@gmail.com

Sensors (Basel, Switzerland)

|November 1, 2012

Summary

This study introduces a novel fruit classification method using multi-class kernel support vector machines (kSVM). The Max-Wins-Voting SVM with a Gaussian Radial Basis kernel achieved 88.2% accuracy, offering an effective computer vision solution.

Keywords:

Unser's texture analysis color histogram fruit classification kernel SVM mathematical morphology multi-class SVM principal component analysis shape feature stratified cross validation

More Related Videos

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

Related Experiment Videos

Last Updated: Dec 19, 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

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Automatic fruit classification is challenging due to diverse fruit properties.
Existing computer vision methods require improvement for accuracy and speed.

Purpose of the Study:

To develop a novel, accurate, and fast fruit classification method.
To evaluate different multi-class support vector machine (SVM) configurations and kernels.

Main Methods:

Fruit images were preprocessed by background removal.
Color, texture, and shape features were extracted and reduced using Principal Component Analysis (PCA).
Multi-class SVMs (Winner-Takes-All, Max-Wins-Voting, Directed Acyclic Graph) with linear, polynomial, and Gaussian Radial Basis kernels were trained and validated.

Main Results:

The Max-Wins-Voting SVM combined with a Gaussian Radial Basis kernel yielded the highest classification accuracy at 88.2%.
Directed Acyclic Graph SVMs demonstrated the fastest computation times.

Conclusions:

The proposed kSVM-based approach offers a robust solution for automatic fruit classification.
Specific SVM and kernel combinations significantly impact classification performance and efficiency.