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

Light Acquisition02:16

Light Acquisition

8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K
Classification of Illness01:17

Classification of Illness

7.5K
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...
7.5K
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

2.6K
Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Hybrid DenseNet-U-Net framework for automated grading of renal cell carcinoma.

Digital health·2026
Same author

Artificial intelligence for early endometrial cancer diagnosis using multimodal clinical data: integrating deep learning, explainability, and data privacy.

Frontiers in artificial intelligence·2026
Same author

PreventativeTestPro: A Scalable Hybrid Testing Framework Utilizing Observability and Generative AI for Proactive Software Quality Engineering.

Journal of visualized experiments : JoVE·2026
Same author

Real-Time Pond Water Assessment via Embedded Deep Learning and Visual Data Acquisition: A Practical Monitoring Approach for Aquaculture.

Journal of visualized experiments : JoVE·2026
Same author

Explainable multi-modal deep learning for transparent cancer diagnosis: integrating radiology, clinical features, and decision visualization.

Frontiers in artificial intelligence·2026
Same author

Intelligent Congestion Control Mechanism for IoT-Enabled Wireless Sensor Networks Using Hybrid Aggregation and Scheduling Technique.

Journal of visualized experiments : JoVE·2026

Related Experiment Video

Updated: Jun 29, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Sandip Thite1, Yogesh Suryawanshi1, Kailas Patil1

  • 1Vishwakarma University, Pune, India.

Data in Brief
|March 27, 2024
PubMed
Summary

A new Sugarcane Leaf Dataset aids in identifying nine diseases and leaf conditions. This resource supports machine learning for improved crop management and higher yields.

Keywords:
ClassificationDatasetDeep learningDisease detectionImage analysisLeaf diseasesMachine learningSugarcane

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.4K

Related Experiment Videos

Last Updated: Jun 29, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.4K

Area of Science:

  • Agricultural Science
  • Plant Pathology
  • Computer Science

Background:

  • Sugarcane is crucial globally, but diseases threaten yield and quality.
  • Accurate disease detection is vital for effective sugarcane management.
  • Existing resources for automated disease identification are limited.

Purpose of the Study:

  • Introduce the "Sugarcane Leaf Dataset" for research.
  • Facilitate the development of machine learning models for sugarcane disease detection.
  • Promote advancements in automated disease identification and crop management.

Main Methods:

  • Compiled a dataset of 6748 high-resolution sugarcane leaf images.
  • Classified images into nine specific disease categories, healthy, and dried leaves.
  • Dataset includes images of diseases like smut, yellow leaf disease, and brown rust.

Main Results:

  • The "Sugarcane Leaf Dataset" is now available.
  • The dataset comprises 6748 labeled images.
  • It covers a comprehensive range of sugarcane leaf pathologies.

Conclusions:

  • The dataset is a valuable resource for machine learning applications in agriculture.
  • It can accelerate research in automated sugarcane disease identification.
  • Open access promotes collaboration for improved crop yields and management.