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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.5K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.5K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

1.9K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
1.9K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

302
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
302
Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

156
Root loci often diverge as system poles shift from the real axis to the complex plane. Key points in this transition are the breakaway and break-in points, indicating where the root locus leaves and reenters the real axis. The branches of the root locus form an angle of 180/n degrees with the real axis, where n is the number of branches at a breakaway or break-in point.
The maximum gain occurs at the breakaway points between open-loop poles on the real axis, while the minimum gain is...
156
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

256
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
256

You might also read

Related Articles

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

Sort by
Same author

Hyperspectral imaging and machine learning for rapid sensing and visualized retrieval of soil nutrients in high-latitude tea-growing regions.

Frontiers in plant science·2026
Same author

RTD-YOLO: a precise quality detection and grading model for rose tea.

NPJ science of food·2026
Same author

Classification of Different Fermentation Stages of Black Tea Using a Lightweight CNN Optimized by Knowledge Distillation.

Foods (Basel, Switzerland)·2026
Same author

Dynamic flavor changes and metabolite profiles of rose petals during black tea processing.

Food research international (Ottawa, Ont.)·2026
Same author

Characterization of volatiles in tea leaf cuticular wax during white tea processing across distinct climatic regions.

Food chemistry: X·2025
Same author

Rapid detection of synthetic pigments in black tea using hyperspectral imaging technology and machine learning.

Food chemistry: X·2025
Same journal

UAV-based temporal synergistic estimation of multiple alfalfa qualities integrating physics-informed network and 3D allometric operator.

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

Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection.

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

FQGR-net: Morphology-based litchi flower quantification and gender recognition.

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

Thermal image segmentation in weedy fields via synthetic RGB-trained models and GAN-based cross-modality alignment.

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

Unlocking almond breeding for nutritional composition with hyperspectral imaging.

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

From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series.

Plant phenomics (Washington, D.C.)·2026
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K

A Tea Buds Counting Method Based on YOLOv5 and Kalman Filter Tracking Algorithm.

Yang Li1, Rong Ma2, Rentian Zhang1,3

  • 1Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China.

Plant Phenomics (Washington, D.C.)
|April 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv5 deep learning model for efficient tea bud counting, improving tea yield estimation. The model accurately detects tea buds in natural light, aiding farmers in management and picking decisions.

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K

Related Experiment Videos

Last Updated: Aug 4, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Manual tea bud counting for yield estimation is labor-intensive and inefficient.
  • Accurate tea yield estimation is crucial for harvest planning and farm management.

Purpose of the Study:

  • To develop an automated and efficient method for tea bud counting using deep learning.
  • To enhance tea yield estimation accuracy and support agricultural decision-making.

Main Methods:

  • An enhanced YOLOv5 model incorporating the Squeeze and Excitation Network was developed.
  • Hungarian matching and Kalman filtering algorithms were integrated for precise bud tracking and counting.
  • The model was trained and validated on field data for tea bud detection.

Main Results:

  • The enhanced YOLOv5 model achieved a mean average precision of 91.88% in detecting tea buds.
  • Model-based counting demonstrated a high correlation (R² = 0.98) with manual counts in video trials.
  • The method proved effective for tea bud detection and counting under natural lighting conditions.

Conclusions:

  • The proposed deep learning approach significantly improves the efficiency and accuracy of tea bud counting.
  • This technology provides valuable data and technical support for rapid tea bud acquisition and yield estimation.
  • The method offers a reliable solution for farmers to optimize management and picking strategies.