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

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

Controlled self-assembly of a pyrene-based bolaamphiphile by acetate ions: from nanodisks to nanofibers by fluorescence enhancement.

Soft matter·2015
Same author

Gradual-order enhanced stability: a frozen section of electrospun nanofibers for energy storage.

Nanoscale·2015
Same author

[Association of human epicardial adipose tissue volume and inflammatory mediators with atherosclerosis and vulnerable coronary atherosclerotic plaque].

Zhonghua xin xue guan bing za zhi·2015
Same author

Ultrasensitive SERS detection of trinitrotoluene through capillarity-constructed reversible hot spots based on ZnO-Ag nanorod hybrids.

Nanoscale·2015
Same author

pERK1/2 silencing sensitizes pancreatic cancer BXPC-3 cell to gemcitabine-induced apoptosis via regulating Bax and Bcl-2 expression.

World journal of surgical oncology·2015
Same author

Probing and controlling liquid crystal helical nanofilaments.

Nano letters·2015
Same journal

Untargeted metabolomics reveals the metabolic basis of sugar-acid balance and quality differentiation in melon.

Frontiers in plant science·2026
Same journal

Biogenic volatile organic compound emission characteristics of dominant tree species in temperate broad-leaved Korean pine forests in Northeast China.

Frontiers in plant science·2026
Same journal

Study on differences in flavonoid synthesis in <i>Xanthoceras sorbifolia</i> leaves based on transcriptome analysis.

Frontiers in plant science·2026
Same journal

Evolutionary diversification of the <i>STAYGREEN</i> gene family in <i>Nicotiana</i>.

Frontiers in plant science·2026
Same journal

Identification and fungicide sensitivity of <i>Monosporascus lespedezae</i> sp. nov. causing root rot of <i>Lespedeza davurica</i> in Gansu, China.

Frontiers in plant science·2026
Same journal

Editorial: Plant phenotyping for agriculture.

Frontiers in plant science·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

半监督学习框架的绩效评估,用于多类杂草检测.

Jiajia Li1, Dong Chen2, Xunyuan Yin3

  • 1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States.

Frontiers in plant science
|September 4, 2024
PubMed
概括
此摘要是机器生成的。

半监督学习显著减少了对标记数据的需求,在精确的杂草管理. 这种方法使用仅10%的标记数据实现了高杂草检测准确度,为除草剂提供了可持续的替代方案.

关键词:
计算机视觉 计算机视觉深度学习是一种深度学习.标签-高效学习学习精准农业 精准农业 精准农业精确的杂草管理 精确的杂草管理

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

相关实验视频

Last Updated: Jun 14, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

科学领域:

  • 农业技术 农业技术
  • 计算机视觉 计算机视觉 计算机视觉
  • 机器学习是机器学习.

背景情况:

  • 精密杂草管理 (PWM) 利用机器视觉和深度学习 (DL) 实现可持续农业.
  • 目前DL杂草检测依赖于监督学习,需要大量的手动数据标签.
  • 标签效率高的方法,特别是半监督学习,正在成为减少数据注释工作的解决方案.

研究的目的:

  • 评估一个半监督的学习框架,用于多类杂草检测.
  • 评估学生-教师框架的有效性,改进伪标签和合奏学生.
  • 用最小的标记数据来展示高性能杂草检测.

主要方法:

  • 实施了半监督学习的一般化学生-教师框架.
  • 对于未标记的数据,使用了改进的伪标签生成模块.
  • 采用一个合奏学生网络来增强模型的概括性.
  • 在FCOS和Faster-RCNN对象检测架构上测试了框架.

主要成果:

  • 在CottonWeedDet3上实现了大约76%的检测准确性,标记数据为10%.
  • 在CottonWeedDet12上达到大约96%的检测准确性,标记数据为10%.
  • 证明性能与使用标签数据少得多的监督方法可比.

结论:

  • 半监督学习为精准农业中杂草检测提供了一种可行和高效的方法.
  • 拟议的框架有效地减少了对大型标记数据集的依赖.
  • 这项研究为推进农业应用中的半监督学习提供了宝贵的资源.