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

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:

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...

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

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,...

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,

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...

You might also read

Related Articles

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

Sort by

Same author

Enhanced Detection of Algal Leaf Spot, Tea Brown Blight, and Tea Grey Blight Diseases Using YOLOv5 Bi-HIC Model with Instance and Context Information.

Plants (Basel, Switzerland)·2025

Same author

ColoPola: A polarimetric imaging dataset for colorectal cancer detection.

GigaScience·2025

Same author

Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression.

Scientific reports·2023

Same author

Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks.

Plants (Basel, Switzerland)·2023

Same author

Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection.

Journal of biomedical optics·2022

Same author

Birefringence effect studies of collagen formed by nonenzymatic glycation using dual-retarder Mueller polarimetry.

Journal of biomedical optics·2022

Search research articles

Related Experiment Video

Updated: Aug 8, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models.

Quoc-Hung Phan¹, Van-Tung Nguyen¹, Chi-Hsiang Lien¹

¹Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan.

Plants (Basel, Switzerland)

|February 25, 2023

Summary

Deep learning models, including Yolov5m with ResNet-101, achieved 100% accuracy classifying ripe and immature tomatoes. These advanced frameworks show promise for automated tomato harvesting systems.

Keywords:

Yolov5 convolution neural network deep learning tomato fruit classification

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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

Related Experiment Videos

Last Updated: Aug 8, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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

Area of Science:

Agricultural technology
Computer vision
Machine learning

Background:

Accurate classification of tomato fruit ripeness and damage is crucial for efficient harvesting.
Automated systems require robust image recognition capabilities.

Purpose of the Study:

To develop and evaluate deep learning frameworks for classifying tomato fruit on the vine.
To compare the performance of Yolov5m, ResNet50, ResNet-101, and EfficientNet-B0 for tomato classification.

Main Methods:

Four deep learning frameworks were proposed: Yolov5m, and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0.
A dataset of 4500 tomato images was used for training over 200 epochs with a batch size of 128 and image size of 224x224 pixels.

Main Results:

Yolo5m combined with ResNet-101 achieved 100% accuracy for ripe and immature tomatoes.
Yolo5m with EfficientNet-B0 reached 94% accuracy for damaged tomatoes.
Testing accuracies for ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 were 98%, 98%, 97%, and 97%, respectively.

Conclusions:

All four proposed deep learning frameworks demonstrate high potential for tomato fruit classification.
These models can support the development of automated tomato fruit harvesting applications in agriculture.