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

8.5K
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.
8.5K
Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

18.7K
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...
18.7K

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

Ionomic Screening of BRRI dhan84 Mutagenized Population Identifies Candidate Genes Underlying High Arsenic and Low Zinc/Cadmium Accumulation.

Physiologia plantarum·2026
Same author

Cytokinin receptor AHK3 influences leaf size by modulating trans-zeatin-type cytokinin levels in xylem.

Plant & cell physiology·2026
Same author

QTL-Seq and pyramiding of yield-related loci from wild rice (<i>Oryza rufipogon</i> Griff.) introgression lines under nutrient-deficient conditions.

Molecular breeding : new strategies in plant improvement·2026
Same author

Establishment of a Non-transgenic Iron-Biofortified Rice Line Using a Novel HRZ1 Mutation.

Rice (New York, N.Y.)·2026
Same author

OsAux1 is required for nutritropism in rice roots.

The Plant journal : for cell and molecular biology·2026
Same author

Integration of proxy intermediate omics traits into a nonlinear two-step model for accurate phenotypic prediction.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026

相关实验视频

Updated: Jun 28, 2025

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.2K

一种多目标回归方法,用高光谱成像技术预测番茄叶中的元素度.

Andrés Aguilar Ariza1, Naoyuki Sotta1, Toru Fujiwara1

  • 1Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.

Plant phenomics (Washington, D.C.)
|April 17, 2024
PubMed
概括

一种新的多目标回归方法显著提高了使用超光谱成像的植物元素度预测准确度. 这种方法提高了作物的营养监测,通过实现10个基本元素的更高准确度.

更多相关视频

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

1.7K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.6K

相关实验视频

Last Updated: Jun 28, 2025

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.2K
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

1.7K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.6K

科学领域:

  • 农业科学 农业科学
  • 遥感 遥感 遥感 遥感
  • 机器学习 机器学习

背景情况:

  • 超光谱成像与机器学习模型相结合,是一种快速,廉价的植物营养状况监测方法.
  • 当前的单目标回归模型预测了单个元素的度,但对不同元素的准确性是可变的.
  • 同时提高多个元素的预测准确度对于全面的作物营养评估至关重要.

研究的目的:

  • 为了提高植物元素度预测的准确性.
  • 评估一种新的多目标回归方法,该方法可以顺序增强高光谱成像特征,并预测元素度.
  • 为了比较多目标方法的性能与传统的单目标回归来预测番茄叶中的17个元素.

主要方法:

  • 开发了一个多目标回归模型,通过顺序增强高光谱成像数据与预测的元素度.
  • 使用增强功能训练了五种不同的机器学习模型.
  • 预测了番茄叶中的17种元素的度,并将结果与单个目标回归进行了比较.

主要成果:

  • 多目标回归方法显著提高了10个元素的预测准确度,包括Mg,P,S,Mn,Fe,Co,Cu,Sr,Mo和Cd.
  • 对几个元素的确定系数 (R2) 显著增加:Mn (12.5%),Cu (10.3%),Co (11%),Fe (10%) 和Mg (8.4%).
  • 在预测元素度方面表现优于单个目标回归,突出显示了顺序增强方法的有效性.

结论:

  • 拟议的多目标回归方法在从高光谱数据中预测植物元素度方面取得了重大进展.
  • 与单一目标方法相比,这种方法提供了更准确和更全面的作物营养状况评估.
  • 这些发现支持采用多目标回归来改善农业监测和管理.