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

Phenotypic alterations and PI3K-AKT pathway regulation in senescence of human tonsil mesenchymal stem cells.

Stem cell research & therapy·2026
Same author

Weed Segmentation in Soybean Fields and Variable-Rate Herbicide Prescription Map Generation Based on UAV Imagery and Improved YOLOv11-seg Model.

Frontiers in plant science·2026
Same author

Multi-omics revealed GOT1/ALDH3A1 pathway attenuated head and neck squamous cell carcinoma and increased cisplatin sensitivity through ROS induced by mitochondrial dysfunction.

Redox report : communications in free radical research·2025
Same author

Improved Predictability of Diagnosis and Prognosis Using Serum- and Tissue-Derived Extracellular Vesicles From Bulk mRNA Sequencing in Pancreatic Ductal Adenocarcinoma.

Cancer medicine·2025
Same author

A Prediction Nomogram of Severe Obstructive Sleep Apnea in Patients with Obesity Based on the Liver Stiffness and Abdominal Visceral Adipose Tissue Quantification.

Nature and science of sleep·2024
Same author

Estimating the direction of arrival of spatially spread sources using block-sparse Bayesian learning with an extended dictionary.

The Journal of the Acoustical Society of America·2024

Related Experiment Video

Updated: Apr 15, 2026

Lateral Root Inducible System in Arabidopsis and Maize
09:23

Lateral Root Inducible System in Arabidopsis and Maize

Published on: January 14, 2016

14.7K

Weed Discrimination at the Seedling Stage in Dryland Fields Under Maize-Soybean Rotation.

Yaohua Yue1, Anbang Zhao2

  • 1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Plants (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

This study introduces an improved YOLOv11n model for accurate weed detection in dryland crop fields, enhancing precision agriculture. The method boosts weed identification accuracy and stability for better herbicide application.

Keywords:
YOLOv11ndynamic convolutionlightweight architecturemaize–soybean rotationseedling-stage weed detectionthe cascaded group attention mechanism

More Related Videos

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
07:09

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines

Published on: January 3, 2014

9.0K
Direct Agroinoculation of Maize Seedlings by Injection with Recombinant Foxtail Mosaic Virus and Sugarcane Mosaic Virus Infectious Clones
05:56

Direct Agroinoculation of Maize Seedlings by Injection with Recombinant Foxtail Mosaic Virus and Sugarcane Mosaic Virus Infectious Clones

Published on: February 27, 2021

6.4K

Related Experiment Videos

Last Updated: Apr 15, 2026

Lateral Root Inducible System in Arabidopsis and Maize
09:23

Lateral Root Inducible System in Arabidopsis and Maize

Published on: January 14, 2016

14.7K
A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
07:09

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines

Published on: January 3, 2014

9.0K
Direct Agroinoculation of Maize Seedlings by Injection with Recombinant Foxtail Mosaic Virus and Sugarcane Mosaic Virus Infectious Clones
05:56

Direct Agroinoculation of Maize Seedlings by Injection with Recombinant Foxtail Mosaic Virus and Sugarcane Mosaic Virus Infectious Clones

Published on: February 27, 2021

6.4K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Remote Sensing

Background:

  • Weed and crop seedlings in dryland rotation systems show high morphological similarity.
  • Complex field conditions (illumination, occlusion, background) hinder accurate weed detection.
  • Existing methods struggle with robust seedling-stage weed identification in UAV imagery.

Purpose of the Study:

  • To develop an improved YOLOv11n-based weed detection method for dryland rotation systems.
  • To enhance detection accuracy and robustness in UAV-acquired field images.
  • To support variable-rate herbicide application and precision field management.

Main Methods:

  • Incorporation of Dynamic Convolution (DynamicConv) for adaptive feature representation.
  • Design of a SlimNeck lightweight feature fusion architecture for efficient multi-scale feature propagation.
  • Integration of Cascaded Group Attention (CGA) into C2PSA for improved background discrimination.

Main Results:

  • The improved YOLOv11n method demonstrated superior performance over baseline models (YOLOv5-v12).
  • Detection accuracy for broadleaf weeds reached 87.2% mAP@0.5.
  • Detection accuracy for Poaceae weeds reached 73.9% mAP@0.5.

Conclusions:

  • The proposed method offers superior accuracy and stability for seedling-stage weed identification in rotation systems.
  • This provides reliable technical support for precision agriculture applications.
  • The enhancements enable more effective variable-rate herbicide application.