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

Leaving Groups02:14

Leaving Groups

9.8K
The nature of leaving groups strongly influences the outcome of a nucleophilic substitution reaction.
In general, in a nucleophilic substitution reaction, a nucleophile displaces a functional group, called the leaving group, from the substrate to give a substituted product. A leaving group departs the substrate molecule through heterolytic cleavage, taking the pair of electrons with it to become a relatively stable weak base in the form of an anion or a neutral molecule.  
In a...
9.8K
Fineness of Cement01:15

Fineness of Cement

539
The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...
539
Fineness Modulus01:19

Fineness Modulus

1.5K
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
1.5K
Segregation in Fresh Concrete01:16

Segregation in Fresh Concrete

609
Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
609
Bleeding in Fresh Concrete01:22

Bleeding in Fresh Concrete

619
Bleeding in fresh concrete occurs when water from the mix rises to the surface. This happens because the mix's solid components fail to retain all the water as they settle, leading to separation where water collects at the top. The severity of bleeding can be measured by assessing the total settlement or by noting the decrease in height per unit height of concrete.
Bleeding can cause several issues in the concrete structure. Sometimes, the rising water gets trapped beneath large aggregate...
619
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.8K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.8K

You might also read

Related Articles

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

Sort by
Same author

Impact of Alkyl Side Chain Length on Morphological Properties and Magnetic Field Response Characteristics of Naphthalenediimide-Based Conjugated Polymer.

Polymers·2026
Same author

Hyaluronic Acid Improves Stability in Ovalbumin-Tea Polyphenol Pickering Particle-Stabilized Gel-like HIPEs via Interfacial Reinforcement.

Gels (Basel, Switzerland)·2026
Same author

REBACIN<sup>®</sup> can remove high-risk Human Papillomavirus (HPV) persistent infection efficiently as an effective non-invasive treatment: a multicenter prospective study.

World journal of surgical oncology·2026
Same author

<i>Glaesserella parasuis</i> Infection Modulates the Transcriptome of Porcine Peritoneal Mesothelial Primary Cells: Implications for Understanding Peritoneal Invasion Mechanisms.

Biology·2026
Same author

Effect of Dryland-to-Paddy Conversion on Soil Aggregate Phosphorus Fractions and Microbial Functional Diversity in a Typical Black Soil Region of the Sanjiang Plain.

Microorganisms·2026
Same author

[Treatment of Paprosky type Ⅲ severe acetabular bone defects with two revision methods].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2026

Related Experiment Video

Updated: Feb 14, 2026

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea Camellia Sinensis L. Leaves
10:35

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea Camellia Sinensis L. Leaves

Published on: June 15, 2019

8.3K

Fine-Grained Detection and Sorting of Fresh Tea Leaves Using an Enhanced YOLOv12 Framework.

Shuang Zhao1, Chun Ye1, Chentao Lian2

  • 1Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China.

Foods (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

This study introduces an enhanced YOLOv12 deep learning model for intelligent grading of fresh tea leaves. The improved framework accurately identifies delicate tea buds, boosting classification accuracy for efficient tea processing.

Keywords:
YOLOv12fine-grained detectionfresh tea leaves sortingmulti-scale attentiontea quality assessment

More Related Videos

Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples
06:04

Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples

Published on: September 28, 2022

2.5K
Determining the Mechanical Strength of Ultra-Fine-Grained Metals
05:04

Determining the Mechanical Strength of Ultra-Fine-Grained Metals

Published on: November 22, 2021

2.6K

Related Experiment Videos

Last Updated: Feb 14, 2026

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea Camellia Sinensis L. Leaves
10:35

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea Camellia Sinensis L. Leaves

Published on: June 15, 2019

8.3K
Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples
06:04

Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples

Published on: September 28, 2022

2.5K
Determining the Mechanical Strength of Ultra-Fine-Grained Metals
05:04

Determining the Mechanical Strength of Ultra-Fine-Grained Metals

Published on: November 22, 2021

2.6K

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Tea quality is directly linked to fresh leaf quality, necessitating advanced sorting methods.
  • Traditional sorting struggles with machine-picked tea leaves due to complex morphology and small, dense targets.
  • Existing intelligent grading systems face challenges in accurate recognition and consistency.

Purpose of the Study:

  • To develop an enhanced deep learning framework for accurate and robust detection of premium tea buds.
  • To improve the classification accuracy and consistency of fresh tea leaves, especially for machine-picked samples.
  • To address challenges like fine-grained differences, small object sizes, and complex backgrounds in tea bud imagery.

Main Methods:

  • Proposed an enhanced YOLOv12 detection framework integrating C3k2_EMA, A2C2f_DYT, and RFAConv modules.
  • Focused on strengthening the model's ability to capture delicate tea bud features.
  • Utilized machine vision and deep learning for automated sorting and grading of fresh tea leaves.

Main Results:

  • Achieved 81.2% precision, 90.6% recall, and 92.7% mAP@0.5 in premium tea recognition.
  • Demonstrated significant improvements in detection accuracy and robustness for fine-grained tea bud identification.
  • Effectively supported intelligent and efficient tea harvesting and sorting operations.

Conclusions:

  • The enhanced YOLOv12 model provides a technically feasible solution for intelligent fresh tea leaves classification.
  • The proposed method effectively addresses the limitations of current automated tea sorting systems.
  • This advancement supports precise quality control and production monitoring in the tea industry.