Jove
Visualize
Contact Us

Related Concept Videos

Classification of Illness01:17

Classification of Illness

8.1K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.1K
Methods of Classification and Identification01:28

Methods of Classification and Identification

472
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...
472
Classification of Leukocytes01:30

Classification of Leukocytes

4.0K
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...
4.0K
Classification of Systems-I01:26

Classification of Systems-I

375
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:
375
Classification of Systems-II01:31

Classification of Systems-II

273
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,
273
Seizures: Classification01:13

Seizures: Classification

834
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
834

You might also read

Related Articles

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

Sort by
Same author

Deep learning-based allergic rhinitis diagnosis using nasal endoscopy images.

Scientific reports·2025
Same author

Efficient Video Compression Using Afterimage Representation.

Sensors (Basel, Switzerland)·2024
Same author

Enhancing Surveillance Systems: Integration of Object, Behavior, and Space Information in Captions for Advanced Risk Assessment.

Sensors (Basel, Switzerland)·2024
Same author

Complex-Phase Steel Microstructure Segmentation Using UNet: Analysis across Different Magnifications and Steel Types.

Materials (Basel, Switzerland)·2023
Same author

Estimation of Number of Pigs Taking in Feed Using Posture Filtration.

Sensors (Basel, Switzerland)·2023
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 Experiment Video

Updated: Oct 30, 2025

Double-Staining Method to Detect Pectin in Plant-Fungus Interaction
06:39

Double-Staining Method to Detect Pectin in Plant-Fungus Interaction

Published on: February 4, 2022

4.8K

Coffee Disease Visualization and Classification.

Milkisa Yebasse1, Birhanu Shimelis2, Henok Warku3

  • 1Department of Computer Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.

Plants (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study visualizes coffee plant diseases using deep learning, improving classification accuracy from 77% to 98%. Visualization techniques help models focus on disease regions, enhancing trust and performance in plant disease detection.

Keywords:
Grad-CAMScore-CAMcoffee disease classificationcoffee disease visualizationdeep learning

More Related Videos

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide
14:29

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide

Published on: March 19, 2018

8.2K
Methodology to Test Control Agents and Insecticides Against the Coffee Berry Borer Hypothenemus hampei
09:23

Methodology to Test Control Agents and Insecticides Against the Coffee Berry Borer Hypothenemus hampei

Published on: March 23, 2022

2.1K

Related Experiment Videos

Last Updated: Oct 30, 2025

Double-Staining Method to Detect Pectin in Plant-Fungus Interaction
06:39

Double-Staining Method to Detect Pectin in Plant-Fungus Interaction

Published on: February 4, 2022

4.8K
The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide
14:29

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide

Published on: March 19, 2018

8.2K
Methodology to Test Control Agents and Insecticides Against the Coffee Berry Borer Hypothenemus hampei
09:23

Methodology to Test Control Agents and Insecticides Against the Coffee Berry Borer Hypothenemus hampei

Published on: March 23, 2022

2.1K

Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Deep learning excels at image classification, including plant disease detection.
  • Current deep learning models for plant diseases often act as black boxes, lacking transparency.
  • Trust in automated disease classification is limited due to potential reliance on irrelevant image features.

Purpose of the Study:

  • To develop and evaluate visualization techniques for understanding deep learning models in coffee disease classification.
  • To gain insights into the regions of coffee plant images that deep learning models focus on during classification.
  • To improve the accuracy and reliability of coffee disease classification through visualization-guided approaches.

Main Methods:

  • Employed visualization methods including Grad-CAM, Grad-CAM++, and Score-CAM.
  • Applied these techniques to a dataset of Robusta coffee leaf images.
  • Developed a guided classification approach informed by visualization insights.

Main Results:

  • Visualization techniques successfully highlighted disease-specific regions in coffee leaf images.
  • Identified misclassifications by analyzing model attention areas.
  • The guided approach significantly improved classification accuracy to 98% from 77% (naïve approach).

Conclusions:

  • Visualization methods offer crucial insights into deep learning model behavior for plant disease detection.
  • A guided approach, informed by visualization, substantially enhances coffee disease classification accuracy.
  • This work promotes more transparent and trustworthy AI applications in agriculture.