Jove
Visualize
Contact Us

Related Concept Videos

Light Acquisition02:16

Light Acquisition

8.4K
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.
8.4K
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
  1. Home
  3. Appleleafnet: A Lightweight And Efficient Deep Learning Framework For Diagnosing Apple Leaf Diseases.
  1. Home
  3. Appleleafnet: A Lightweight And Efficient Deep Learning Framework For Diagnosing Apple Leaf Diseases.

Related Experiment Video

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

28.3K

AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases.

Muhammad Umair Ali1, Majdi Khalid2, Majed Farrash2

  • 1Department of Artificial Intelligence and Robotics, Sejong University, Seoul, Republic of Korea.

Frontiers in Plant Science
|December 12, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

A new lightweight deep learning model accurately identifies apple leaf diseases. This two-stage approach achieves high accuracy in detecting healthy or diseased leaves and diagnosing specific conditions like rust and scab.

Keywords:
apple leaf condition identificationapple leaf disease detectioncrop monitoringdeep learninglightweight model

More Related Videos

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.4K
Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding
09:31

Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding

Published on: September 20, 2024

592

Related Experiment Videos

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

28.3K
Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.4K
Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding
09:31

Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding

Published on: September 20, 2024

592

Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Accurate apple disease identification is vital for industry sustainability and apple quality.
  • Analyzing complex leaf images for disease detection presents significant computational challenges.
  • Existing deep learning models can be resource-intensive for practical field applications.

Purpose of the Study:

  • To develop a novel, lightweight deep learning model for efficient apple leaf disease identification.
  • To implement a two-stage framework for initial condition assessment and subsequent disease subclassification.
  • To evaluate the model's performance using a publicly available dataset.

Main Methods:

  • A custom 37-layer lightweight deep learning model was designed from scratch.
  • The model was first trained to classify leaves as healthy or diseased.
  • Transfer learning was applied using the trained model for subclassification of specific diseases (rust, complex, scab, frogeye).

    • Main Results:

    • The two-stage framework achieved 98.25% accuracy in identifying apple leaf conditions.
    • The model demonstrated 98.60% accuracy in diagnosing specific apple leaf diseases.
    • The developed model is significantly lighter with fewer learnable parameters compared to pre-trained models.

    Conclusions:

    • The proposed lightweight deep learning model offers an effective and efficient solution for apple disease identification.
    • The two-stage approach enhances diagnostic precision for various apple leaf conditions.
    • This model presents a practical tool for improving apple production and disease management.