Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
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

相关实验视频

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

多种作物的多传感器和多时间数据集用于现场表型化和监测.

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
概括
此摘要是机器生成的。

一个新的数据集,MuST-C,提供自动作物表型化解决方案. 这种多传感器,多时间数据集有助于开发先进的作物特征估计方法,克服传统的监测局限性.

更多相关视频

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

相关实验视频

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

科学领域:

  • 农业科学 农业科学
  • 机器人技术 机器人技术 机器人技术
  • 数据科学数据科学数据科学

背景情况:

  • 传统的作物表型是昂贵和劳动密集型的,阻碍了研究进展.
  • 需要自动化方法来克服"表型化瓶".
  • 数据驱动的方法需要全面的数据集来开发新的表型技术.

研究的目的:

  • 介绍 MuST-C 数据集用于自动作物表型化.
  • 促进新型表型算法的开发和验证.
  • 实现交叉传感器和交叉作物通用性研究.

主要方法:

  • 收集了六种作物物种在一个生长季节的现场数据.
  • 利用了带有RGB,LiDAR和多谱传感器的空中和地面机器人平台.
  • 地理引用所有传感器数据以实现时间和空间对齐.
  • 嵌入了手动测量叶面积指数和生物质作为地面真相.

主要成果:

  • MuST-C数据集提供了地理引用,多传感器,多时间数据.
  • 包括六种不同的作物种的数据.
  • 包含传感器读数和手动参考测量.

结论:

  • MuST-C数据集支持自动化表型特征估计的发展.
  • 它允许对不同传感器和方法进行比较分析.
  • 该数据集促进了对各种作物的可概括表型研究.