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 Experiment Video

Updated: Jul 10, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

LeafLiteX mobile application for leaf disease detection using U-Net segmentation and lightweight deep learning.

Pawan Kumar Verma1,2, Divya Midhun3, Hemalatha4

  • 1Lincoln University College, Petaling Jaya, Malaysia. abes.pawan@gmail.com.

Scientific Reports
|July 8, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Microsurgical clipping of paraclinoid aneurysms: a 12-year single-centre experience with surgical nuances and outcomes.

Neurosurgical review·2026
Same author

Screw-Lock: A Novel Technique to Secure Burr Hole Base Ring in DBS Surgery.

Surgical innovation·2026
Same author

Spatiotemporal deep learning for real-time video-based deepfake detection using 3DCNN, 3DResNet, TCN, and VAE.

Scientific reports·2026
Same author

Diagnosis and therapeutic outcomes of biopsy-confirmed Pure Neuritic Leprosy: A two-decade experience.

PLoS neglected tropical diseases·2026
Same author

Comprehensive review of heart disease prediction: A comparative study from 2019 onwards.

Artificial intelligence in medicine·2026
Same author

Tailored vertebral artery mobilisation in complex craniovertebral junction surgery.

Neurosurgical review·2026
This summary is machine-generated.

This study presents LeafLiteX, a mobile app for real-time plant leaf disease detection. It uses deep learning for accurate, on-device diagnosis, aiding farmers in sustainable agriculture.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Plant diseases pose a significant threat to global food production and economic stability.
  • Timely and accurate identification of leaf diseases is crucial for preventing crop loss and ensuring sustainable agriculture.

Purpose of the Study:

  • To introduce LeafLiteX, a lightweight, mobile-based deep learning application for real-time plant leaf disease detection and classification.
  • To develop an efficient, on-device diagnostic tool for farmers to support early disease detection and decision-making.

Main Methods:

  • The LeafLiteX application employs U-Net segmentation for precise leaf region identification.
  • MobileNetV3-Large is utilized for rapid disease classification with reduced computational demands.
Keywords:
Leaf disease detectionLightweight model optimizationMobile deep learningSmart agricultureU-Net segmentation

Related Experiment Videos

Last Updated: Jul 10, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

  • The system performs end-to-end processing, including image acquisition, segmentation, and prediction, directly on mobile devices.

    • Main Results:

    • The model achieved a high accuracy of 98.85% on diverse crop disease datasets.
    • The application demonstrated improved generalization capabilities with minimal latency.
    • The system is robust to variations in lighting, background noise, and camera resolution, ensuring reliable on-device inference.

    Conclusions:

    • LeafLiteX offers a low-cost, user-friendly, offline-capable diagnostic solution for farmers, supporting in-the-moment decision-making.
    • The research highlights the potential of edge-optimized machine learning and computer vision in advancing smart agriculture technologies.
    • This study emphasizes practical implementation through segmentation, lightweight classification, and explainability in a mobile-friendly model.