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

Fruit Development, Structure, and Function01:58

Fruit Development, Structure, and Function

22.4K
Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.
22.4K
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
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

195
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
195
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.7K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.7K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

487
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...
487

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

Far-red light in early growth stages boosts lettuce biomass and preserves anthocyanins.

Annals of botany·2026
Same author

A Connected Building Landscape dataset for Instance Segmentation.

Scientific data·2025
Same author

Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset.

Sensors (Basel, Switzerland)·2025
Same author

Multi-Scale Attention Network for Vertical Seed Distribution in Soybean Breeding Fields.

Plant phenomics (Washington, D.C.)·2024
Same author

EasyDAM_V4: Guided-GAN-based cross-species data labeling for fruit detection with significant shape difference.

Horticulture research·2024
Same author

DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling.

Plant phenomics (Washington, D.C.)·2024

相关实验视频

Updated: Jul 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

291

EasyDAM_V3:基于最佳源域选择和通过知识图进行数据合成的自动水果标签.

Wenli Zhang1, Yuxin Liu1, Chao Zheng1

  • 1Information Department, Beijing University of Technology, Beijing 100022, China.

Plant phenomics (Washington, D.C.)
|July 31, 2023
PubMed
概括

本研究介绍了EasyDAM_V3模型用于自动水果标签,大大降低了手工工作和成本. 该模型实现了高注释精度,使深度学习果实检测更容易获得.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

21.6K

相关实验视频

Last Updated: Jul 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

291
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

21.6K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 农业技术 农业技术

背景情况:

  • 果实检测的深度学习需要大量的标记数据,这是昂贵的和耗时的获取.
  • 降低标签成本的现有方法,如生成对抗网络,在跨物种适用性和完全自动化过程方面存在局限性.

研究的目的:

  • 开发一种改进的自动标签方法 (EasyDAM_V3),用于水果检测,消除手动标签并降低成本.
  • 使用多维空间特征模型建立一个最佳的源域选择方法.
  • 创建一个大容量数据集的构建方法,利用透明的背景水果图像翻译和知识图.

主要方法:

  • 提出了EasyDAM_V3模型用于自动获取水果标签.
  • 根据多维空间特征开发了一个最佳的源域选择策略.
  • 实施了一种数据集构建方法,涉及透明的背景水果图像翻译和知识图表合成.

主要成果:

  • 该EasyDAM_V3模型成功实现了水果标签的自动化,消除了手动注释的需要.
  • 实现了高平均注释精度:子为90.94%,果为89.78%,番茄为90.84%.
  • 证明模型能够识别目标数据集的最佳源域 ().

结论:

  • 该EasyDAM_V3模型有效地自动化水果标签任务,显著减少手工劳动和相关成本.
  • 建议的最佳源域选择和数据集构建的方法提高了自动标签的效率和准确性.
  • 这种方法通过克服数据注释瓶,使得基于深度学习的果实检测更加可行.