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

9.4K
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.
9.4K
Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

23.9K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
23.9K

You might also read

Related Articles

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

Sort by
Same author

Sun-induced fluorescence responses to structural and physiological effects caused by the Cercospora leaf spot in sugar beet.

Journal of experimental botany·2026
Same author

Influence of red- and blue-dominant light spectra on the biosynthesis of non-volatile secondary metabolites in Mentha spp.

Food chemistry·2026
Same author

Toward Generating Realistic 3D Semantic Training Data for Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Generation of labeled leaf point clouds for plants trait estimation.

Plant phenomics (Washington, D.C.)·2025
Same author

Engineering vascular potassium transport increases yield and drought resilience of cassava.

Nature plants·2025
Same author

Influence of red- and blue-dominant light spectra on the biosynthesis of mono- and sesquiterpenes in mint (Mentha × piperita) essential oil.

Food chemistry·2025
Same journal

Dataset of Optimized Structures of Aliphatic Chains Chemisorbed on Si(110) and Si(111) Surfaces via First-Principles Methods.

Scientific data·2026
Same journal

EURO-PROBE - Manual segmentations of the prostate and intraprostatic urethra on T2-weighted MRI.

Scientific data·2026
Same journal

Chromosome-Level Genome Assembly of Southern Africa Mozambique Tilapia (Oreochromis mossambicus) using PacBio HiFi and Omni-C sequencing.

Scientific data·2026
Same journal

Ovarian Stainology: Database of evidence-based immunohistochemical antigen expression in ovarian tumors.

Scientific data·2026
Same journal

A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations.

Scientific data·2026
Same journal

A real-world Fitbit-derived dataset of activity, sleep, and heart rate with matched clinical factors in on-treatment lung cancer patients.

Scientific data·2026
See all related articles

Related Experiment Video

Updated: Jan 13, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

12.4K

The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring.

Yue Linn Chong1, Julie Krämer2, Erekle Chakhvashvili2

  • 1Center for Robotics, University of Bonn, 53115, Bonn, Germany. linn.chong@uni-bonn.de.

Scientific Data
|January 8, 2026
PubMed
Summary
This summary is machine-generated.

A new dataset, MuST-C, offers automated crop phenotyping solutions. This multi-sensor, multi-temporal dataset aids in developing advanced crop trait estimation methods, overcoming traditional monitoring limitations.

More Related Videos

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

9.8K
Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
06:28

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

2.6K

Related Experiment Videos

Last Updated: Jan 13, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

12.4K
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

9.8K
Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
06:28

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

2.6K

Area of Science:

  • Agricultural Science
  • Robotics
  • Data Science

Background:

  • Traditional crop phenotyping is costly and labor-intensive, hindering research progress.
  • Automated methods are needed to overcome the 'phenotyping bottleneck'.
  • Data-driven approaches require comprehensive datasets for developing new phenotyping technologies.

Purpose of the Study:

  • Introduce the MuST-C dataset for automated crop phenotyping.
  • Facilitate the development and validation of novel phenotyping algorithms.
  • Enable cross-sensor and cross-crop generalizability studies.

Main Methods:

  • Collected field data over a growing season for six crop species.
  • Utilized aerial and ground robotic platforms with RGB, LiDAR, and multispectral sensors.
  • Georeferenced all sensor data for temporal and spatial alignment.
  • Incorporated manual measurements of leaf area index and biomass as ground truth.

Main Results:

  • The MuST-C dataset provides georeferenced, multi-sensor, multi-temporal data.
  • Includes data from six diverse crop species.
  • Contains both sensor readings and manual reference measurements.

Conclusions:

  • The MuST-C dataset supports the development of automated phenotypic trait estimation.
  • It allows for comparative analysis of different sensors and methods.
  • The dataset promotes research into generalizable phenotyping across various crops.