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

Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K
Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

19.8K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
19.8K
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

159
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
159
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

721
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
721
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

536
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
536

You might also read

Related Articles

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

Sort by
Same author

Sensitive and Selective Carmine Acid Detection Based on Chemiluminescence Quenching of Layer Doubled Hydroxide-Luminol-H<sub>2</sub>O<sub>2</sub> System.

ACS omega·2019
Same author

Elucidating Energy Pathways through Simultaneous Measurement of Absorption and Transmission in a Coupled Plasmonic-Photonic Cavity.

Nano letters·2019
Same author

Artificial Solid-Electrolyte Interface Facilitating Dendrite-Free Zinc Metal Anodes via Nanowetting Effect.

ACS applied materials & interfaces·2019
Same author

Revealing Insights into Li<sub></sub>FePO<sub>4</sub> Nanocrystals with Magnetic Order at Room Temperature Resulting in Trapping of Li Ions.

The journal of physical chemistry letters·2019
Same author

Spontaneous cellular vibratory motions of osteocytes are regulated by ATP and spectrin network.

Bone·2019
Same author

Association between metabolic syndrome and knee structural change on MRI.

Rheumatology (Oxford, England)·2019

Related Experiment Video

Updated: Sep 14, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.7K

Leaf area estimation in small-seeded broccoli using a lightweight instance segmentation framework based on improved

Yaben Zhang1, Yifan Li1, Xiaowei Cao1

  • 1College of Engineering, Nanjing Agricultural University, Nanjing, China.

Frontiers in Plant Science
|July 21, 2025
PubMed
Summary

A new lightweight model, YOLOv11-AreaNet, accurately measures broccoli leaf area for early crop phenotyping. This efficient system enables real-time monitoring and high-throughput screening in agriculture.

Keywords:
broccoli seedlingsimproved YOLOv11leaf area segmentationlightweight modelplant trait quantificationsmart agriculture

More Related Videos

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy
06:28

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

3.5K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K

Related Experiment Videos

Last Updated: Sep 14, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.7K
Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy
06:28

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

3.5K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Phenotyping

Background:

  • Accurate leaf area measurement is crucial for early phenotyping in small-seeded crops like broccoli.
  • Traditional methods are inefficient due to dense, overlapping, and irregular foliage.

Purpose of the Study:

  • To develop a lightweight instance segmentation model for precise leaf area estimation in broccoli seedlings.
  • To optimize spatial sensitivity, multiscale fusion, and computational efficiency for real-time applications.

Main Methods:

  • Utilized YOLOv11-AreaNet with an EfficientNetV2 backbone, Focal Modulation, C2PSA-iRMB attention, LDConv, and CCFM modules.
  • Trained on 5,000 augmented images from a custom phenotyping system.
  • Applied post-processing including morphological optimization, edge enhancement, and watershed segmentation.

Main Results:

  • YOLOv11-AreaNet achieved comparable segmentation accuracy to YOLOv11 with 57.4% fewer parameters and 25.9% fewer operations.
  • Model size was reduced by 51.7%, enabling edge device deployment.
  • High correlation (R² = 0.983) with manual measurements confirmed precision; relative leaf area growth rates reached 26.6%.

Conclusions:

  • YOLOv11-AreaNet provides a robust and scalable solution for automated leaf area measurement in small-seeded crops.
  • The system supports high-throughput screening and intelligent crop monitoring.
  • Enables real-time deployment on edge devices for agricultural applications.