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 Videos

ZDAM: a new deep learning model for bean leaf disease diagnosis.

Jia Liu1,2, Kaidi Yu1, Hongyun Song1

  • 1School of Computer and Artificial Intelligence, Henan University of Urban Construction, Pingdingshan, China.

Frontiers in Plant Science
|June 29, 2026
PubMed
Summary

Related Concept Videos

Light Acquisition02:16

Light Acquisition

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.

You might also read

Related Articles

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

Sort by
Same author

ViTKAB: an efficient deep learning network for cotton leaf disease identification.

Frontiers in plant science·2025
Same author

Corrigendum to "Benzene induces myelodysplasia and hematotoxicity via ferroptosis induction and downregulation of GPX4" [Arch. Biochem. Biophys. 773 (2025) 110606].

Archives of biochemistry and biophysics·2025
Same author

Benzene induces myelodysplasia and hematotoxicity via ferroptosis induction and downregulation of GPX4.

Archives of biochemistry and biophysics·2025
Same author

Anti-tumor and immunomodulation activity of polysaccharide from Paris polyphylla Smith var. chinensis in S180 tumor-bearing mice.

Journal of the science of food and agriculture·2025
Same author

Traditional Chinese medicine for sepsis: advancing from evidence to innovative drug discovery.

Critical care (London, England)·2025
Same author

Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation.

Sensors (Basel, Switzerland)·2025

A new deep learning model, ZDAM, accurately identifies crop diseases from leaf images, achieving 99.02% accuracy. This automated disease monitoring supports sustainable agriculture and reduces postharvest losses.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Accurate crop disease diagnosis is vital for agricultural productivity and food safety.
  • Traditional methods struggle with complex field conditions and extensive feature modeling.

Purpose of the Study:

  • To develop an advanced deep learning model for precise crop disease identification.
  • To improve automated disease monitoring systems for enhanced agricultural management.

Main Methods:

  • Proposed a deep learning model, ZDAM, utilizing an optimized ZFNet with a dual attention mechanism.
  • Integrated channel and spatial attention, along with a residual module, to refine feature extraction and boost accuracy.

Main Results:

Keywords:
attention mechanismbean leafdeep learningdisease identificationsmart agriculture

Related Experiment Videos

  • The ZDAM model achieved an average recognition accuracy of 99.02% on a dataset of 11,903 bean leaf images.
  • Outperformed existing models like MobileMamba, Vision Transformer, and Chest-OMD in disease identification.

    • Conclusions:

    • The ZDAM model provides a scalable and accurate solution for automated crop disease monitoring.
    • This technology aids in preserving postharvest quality and promoting sustainable crop production.