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

8.4K
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

You might also read

Related Articles

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

Sort by
Same author

Modeling and experimental research on residual height of aspheric arc envelope grinding.

Applied optics·2026
Same author

Key role of moss in supplementing nitrogen for plant growth under warming in a permafrost ecosystem.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Chemoautotrophic carbon fixation in thermokarst lakes on the Tibetan Plateau.

Nature communications·2025
Same author

Multilayer Perceptron Grouping and Sparse Gaussian Process-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Multiobjective Optimization.

IEEE transactions on cybernetics·2025
Same author

Grassland degradation alters plant and soil biodiversity-multifunctionality relationships.

Nature plants·2025
Same author

SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigor in wheat and other cereal crops using deep learning-powered dynamic phenotypic analysis.

GigaScience·2025

Related Experiment Video

Updated: Jun 11, 2025

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

Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8.

Yuelong He1,2, Yunfeng Peng1,2, Chuyong Wei1,2

  • 1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

Plants (Basel, Switzerland)
|September 28, 2024
PubMed
Summary

Timely detection of strawberry leaf diseases is crucial for yield. A new KTD-YOLOv8 model enhances disease identification accuracy and speed, offering a valuable tool for intelligent plant monitoring systems.

Keywords:
deep learningsmart agriculturestrawberry diseasetarget detection

More Related Videos

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
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

6.8K

Related Experiment Videos

Last Updated: Jun 11, 2025

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
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
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

6.8K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Pathology

Background:

  • Strawberry cultivation faces significant yield and quality losses due to various diseases.
  • Early and accurate disease detection in strawberry leaves is essential for effective management.
  • Existing automated methods may lack the necessary speed and accuracy for real-time field applications.

Purpose of the Study:

  • To develop an automated system for accurate and rapid identification of diseases in strawberry leaves.
  • To enhance the performance of object detection models for agricultural applications.
  • To improve precision agriculture tools for strawberry crop monitoring.

Main Methods:

  • Introduction of the KTD-YOLOv8 model, integrating KernelWarehouse convolution and Triplet Attention mechanism.
  • Replacement of traditional YOLOv8 backbone components to reduce computational complexity.
  • Construction of a parameter-sharing diverse branch block (DBB) sharing head for improved multi-scale feature processing.

Main Results:

  • The KTD-YOLOv8 model demonstrated a 2.8% increase in average accuracy compared to the original YOLOv8.
  • A significant reduction of 38.5% in floating-point calculations was achieved.
  • Enhanced ability to process targets at different spatial scales was observed.

Conclusions:

  • The KTD-YOLOv8 model offers a significant improvement in accuracy and computational efficiency for strawberry leaf disease detection.
  • This model provides a viable new option for intelligent plant monitoring and precision pesticide spraying systems.
  • The enhanced model contributes to more effective and sustainable strawberry farming practices.