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

You might also read

Related Articles

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

Sort by
Same author

Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation.

Plant phenomics (Washington, D.C.)·2026
Same author

Self-Guided Internet-Based Mindfulness-Informed Stress Management for Generalized Anxiety Disorder: Randomized Controlled Trial With Longitudinal Network Analysis.

Journal of medical Internet research·2026
Same author

A porcine circovirus type 2d-based virus-like particle subunit vaccine effectively protects pigs against homologous challenge.

Frontiers in microbiology·2026
Same author

ProtSATT: An Advanced Protein Solubility Predictor Based on Attention Mechanism.

Journal of chemical information and modeling·2026
Same author

WPDSI: A deep learning method for wheat phenology detection from single-temporal images.

Plant phenomics (Washington, D.C.)·2026
Same author

Antenna Effect in Halogen-Containing ZnSm Coordination Compounds: Utilizing Colorimetry for a Room-Temperature Tunable Ratiometric Molecular Thermometer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Related Experiment Video

Updated: Jul 4, 2025

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

1.5K

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices.

Peisen Yuan1, Ye Xia1, Yongchao Tian1,2

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.

Frontiers in Plant Science
|February 8, 2024
PubMed
Summary

Accurate rice disease classification is crucial for phenotyping. This study introduces a novel framework using transfer learning and SENet with an attention mechanism, achieving 95.73% accuracy in identifying diseases like Bacterial blight and Blast.

Keywords:
SENetmachine learning as servicemicroservices frameworkrice disease recognitiontransfer learning

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

755
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Related Experiment Videos

Last Updated: Jul 4, 2025

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

1.5K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

755
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Accurate rice disease classification is vital for crop phenotyping and management.
  • High phenotypic similarity among diseases like Bacterial blight, Blast, Brownspot, Leaf smut, and Tungro presents a significant identification challenge.

Purpose of the Study:

  • To develop an effective framework for rice disease phenotype identification.
  • To improve the accuracy and efficiency of recognizing various rice diseases.

Main Methods:

  • Utilized transfer learning by optimizing pre-trained parameters for the SENet network.
  • Incorporated an attention mechanism within SENet for enhanced feature extraction.
  • Developed a cloud-based platform using microservices architecture for disease recognition as a service.

Main Results:

  • Achieved a high accuracy of 0.9573 in rice disease classification.
  • Demonstrated the effectiveness of the proposed framework in capturing distinctive disease features.
  • Successfully deployed a functional cloud platform for accessible rice disease phenotype recognition.

Conclusions:

  • The proposed framework effectively addresses the challenge of classifying phenotypically similar rice diseases.
  • The integration of transfer learning and attention mechanisms significantly improves recognition accuracy.
  • The cloud-based microservices platform offers a scalable and user-friendly solution for rice disease identification.