Jove
Visualize
Contact Us

Related Concept Videos

Plant Tissues01:18

Plant Tissues

10.0K
Plants are multicellular eukaryotes with tissue systems made of various cell types that carry out specific functions. Different tissues work together to perform a unique function and form an organ. Organs working together form organ systems. Vascular plants have two distinct organ systems: a shoot system and a root system. The shoot system consists of two portions: the vegetative (non-reproductive) parts of the plant, such as the leaves and the stems, and the reproductive parts of the plant,...
10.0K
Differential Staining Technique01:26

Differential Staining Technique

2.9K
Differential staining is an essential microbiological technique that exploits variations in cell wall structures to classify and identify microorganisms. It facilitates the distinction of bacteria, aiding in diagnostic and research applications. Two of the most widely used differential staining methods are Gram staining and acid-fast staining, both of which rely on the chemical and structural differences in bacterial cell walls.Gram Staining TechniqueGram staining differentiates bacteria by...
2.9K
Frost Circles for Different Conjugated Systems01:18

Frost Circles for Different Conjugated Systems

4.3K
The inscribed polygon method is consistent with Hückel’s 4n + 2 rule and helps to learn whether the given cyclic compound is aromatic or not. The compound is stable and aromatic if every bonding molecular orbital (MO) is completely filled with a pair of electrons. However, if the non-bonding or antibonding orbitals are filled with electrons, the compound is unstable and not aromatic. Consider the Frost circle diagrams for cycloalkenes containing 4 to 8 carbons.
4.3K
Fixation and Sectioning01:03

Fixation and Sectioning

8.9K
Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
8.9K
Simple Staining Technique01:24

Simple Staining Technique

5.3K
OverviewStaining techniques in microscopy enhance the visualization of microorganisms by increasing contrast and allowing the differentiation of cellular structures. Simple staining is one of the fundamental methods used to observe the basic morphological characteristics of microorganisms, including their size, shape, and arrangement. This method relies on the application of a single dye to stain the entire cell, producing a clear contrast between the cell and the background.FixationFixation is...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Knowledge distillation and pseudo-labeling for lightweight YOLOv11-based structural crack detection.

Scientific reports·2026
See all related articles
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 Video

Updated: Apr 13, 2026

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

11.1K

Frost damage segmentation in grapevine organs using YOLOv11s with ASPP and dynamic confidence thresholding.

Kaan Arık1,2, Erdal Büyükbıçakcı3,4

  • 1Information Technologies of Vocational School, Sakarya University of Applied Sciences, Sakarya, Turkey. kaanarik@subu.edu.tr.

Scientific Reports
|April 11, 2026
PubMed
Summary

This study introduces an AI framework using YOLOv11s with ASPP to accurately detect frost damage in vineyards. The system provides early, efficient frost damage assessment for viticulture, improving crop management.

Keywords:
Atrous spatial pyramid poolingDynamic confidence thresholdingFrost damage recognitionGrapevine organ segmentationViticultureYOLOv11

More Related Videos

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.3K
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.1K

Related Experiment Videos

Last Updated: Apr 13, 2026

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

11.1K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.3K
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.1K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Climate change increases spring frost events, threatening viticulture yield and quality.
  • Early detection of frost damage is crucial for effective vineyard management and mitigation strategies.

Purpose of the Study:

  • To develop an AI-powered framework for early, rapid, and accurate segmentation of frost damage in grapevines.
  • To enhance object detection models for improved performance in complex vineyard environments.

Main Methods:

  • Utilized YOLOv11s object detection model enhanced with Atrous Spatial Pyramid Pooling (ASPP) for feature extraction.
  • Developed the FGVL dataset with manually annotated frost-damaged and healthy grapevine organs.
  • Implemented Dynamic Confidence Thresholding (DCT) to improve prediction reliability.

Main Results:

  • Achieved a mean Average Precision (mAP@50) of 0.7686 in segmenting frost-damaged grapevine organs.
  • Demonstrated robust performance in identifying small, overlapping, and visually similar structures.
  • Maintained stable performance with low computational demand (6.45 GB GPU memory).

Conclusions:

  • The proposed framework offers an accurate, efficient, and deployable solution for early frost damage recognition in viticulture.
  • The integration of ASPP and DCT enhances the model's capability to handle challenging vineyard conditions.