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

Arthroscopic Superior Capsule Reconstruction With Combined Fascia Lata Autograft Augmented With Either LARS Ligament or Polypropylene Mesh Synthetic Scaffold Patch Graft: Letter to the Editor.

The American journal of sports medicine·2026
Same author

Current Progress in the Role of Ferroptosis in Skeletal Muscle Atrophy.

Mediators of inflammation·2026
Same author

Replicating the post-chemotherapy tumor microenvironment <i>via</i> biomimetic scaffolds to regulate MSC differentiation.

RSC advances·2026
Same author

Synthesis, Sintering, and Characterization of Composites.

Materials (Basel, Switzerland)·2026
Same author

Non-classic deubiquitinase USP13 inhibits bladder cancer metastasis through destabilizing cytoplasmic KDM3A.

Oncogene·2026
Same author

Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy.

Frontiers in medicine·2026

Related Experiment Video

Updated: Jun 14, 2025

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana
08:52

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana

Published on: November 9, 2013

13.5K

RT-DETR-SoilCuc: detection method for cucumber germinationinsoil based environment.

Zhengjun Li1, Yijie Wu1, Haoyu Jiang1

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

Frontiers in Plant Science
|September 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces RT-DETR-SoilCuc, a lightweight deep learning model for accurate cucumber seed germination detection in soil. It significantly improves upon existing methods, reducing manual labor and aiding new variety selection.

Keywords:
RT-DETRcucumber germinationgermination ratesalt tolerancesoil-based environment

More Related Videos

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
07:03

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions

Published on: November 6, 2016

10.5K
A Hydroponic Co-cultivation System for Simultaneous and Systematic Analysis of Plant/Microbe Molecular Interactions and Signaling
11:16

A Hydroponic Co-cultivation System for Simultaneous and Systematic Analysis of Plant/Microbe Molecular Interactions and Signaling

Published on: July 22, 2017

14.0K

Related Experiment Videos

Last Updated: Jun 14, 2025

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana
08:52

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana

Published on: November 9, 2013

13.5K
Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
07:03

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions

Published on: November 6, 2016

10.5K
A Hydroponic Co-cultivation System for Simultaneous and Systematic Analysis of Plant/Microbe Molecular Interactions and Signaling
11:16

A Hydroponic Co-cultivation System for Simultaneous and Systematic Analysis of Plant/Microbe Molecular Interactions and Signaling

Published on: July 22, 2017

14.0K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Breeding

Background:

  • Current deep learning models for seed germination detection perform poorly in complex soil environments.
  • Traditional manual methods are labor-intensive, time-consuming, and error-prone, especially in soil cultivation.

Purpose of the Study:

  • To develop an accurate and efficient method for detecting cucumber seed germination in soil-based environments.
  • To address the limitations of existing technologies in real-world agricultural conditions.
  • To facilitate the selection and breeding of new cucumber varieties.

Main Methods:

  • Developed a Seed Germination Phenotyping System for a soil-based cucumber germination environment under salt stress.
  • Constructed a cucumber germination dataset and designed a lightweight real-time detection model, RT-DETR-SoilCuc, based on Real-Time DEtection TRansformer (RT-DETR).
  • Enhanced the model with online image enhancement, Adown downsampling, Generalized Efficient Lightweight Network backbone, Online Convolutional Re-parameterization, and Normalized Gaussian Wasserstein Distance loss.

Main Results:

  • The RT-DETR-SoilCuc model achieved significant lightweighting, with 61.2% fewer parameters, 61% reduction in FLOPs, and 56.5% reduction in weight size compared to RT-DETR-R18.
  • Achieved high detection accuracy with mAP@0.5 of 98.2%, precision of 97.4%, and recall of 96.9%.
  • Demonstrated superior performance over similar-sized You Only Look Once models and validated accuracy in detecting embryonic root targets amidst soil interference.

Conclusions:

  • The RT-DETR-SoilCuc model offers a highly accurate and efficient solution for cucumber germination detection in complex soil environments.
  • This technology significantly reduces manual workload in monitoring germination and aids in the efficient selection and breeding of new cucumber varieties.
  • The model's robustness against soil background interference provides a valuable tool for agricultural research and practice.