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

8.4K
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.
8.4K

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

Effect of angiotensin II and angiotensin II type 1 receptor antagonist on the proliferation, contraction and collagen synthesis in rat hepatic stellate cells.

Chinese medical journal·2008
Same author

[Determination of nicotinamide in formula milk powder using liquid chromatography-isotope dilution mass spectrometry].

Se pu = Chinese journal of chromatography·2008
Same author

In vivo tracking of superparamagnetic iron oxide nanoparticle-labeled mesenchymal stem cell tropism to malignant gliomas using magnetic resonance imaging. Laboratory investigation.

Journal of neurosurgery·2008
Same author

Enhancement and broadening of extreme-ultraviolet supercontinuum in a relative phase controlled two-color laser field.

Optics letters·2008
Same author

Screening and breeding of high taxol producing fungi by genome shuffling.

Science in China. Series C, Life sciences·2008
Same author

Reversible self-association of a concentrated monoclonal antibody solution mediated by Fab-Fab interaction that impacts solution viscosity.

Journal of pharmaceutical sciences·2008

相关实验视频

Updated: Jun 6, 2025

An Effective Inoculation Method for Phytophthora capsici on Black Pepper Plants
08:58

An Effective Inoculation Method for Phytophthora capsici on Black Pepper Plants

Published on: September 16, 2022

3.3K

一个多式调节框架,用于检测胡病和害虫.

Jun Liu1, Xuewei Wang2

  • 1Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China. liu_jun860116@wfust.edu.cn.

Scientific reports
|November 23, 2024
PubMed
概括

一个新的PepperNet模型使用自然语言描述在复杂的图像中准确地检测到胡病和害虫. 这种先进的物体检测方法实现了高精度和速度,即使在具有挑战性的噪音和阻塞的情况下.

关键词:
多式联络是多式联络.自然语言自然语言对象检测检测对象检测对象检测胡的疾病和害虫图片 胡的疾病和害虫图片视觉特征 视觉特征 视觉特征

更多相关视频

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.6K
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

相关实验视频

Last Updated: Jun 6, 2025

An Effective Inoculation Method for Phytophthora capsici on Black Pepper Plants
08:58

An Effective Inoculation Method for Phytophthora capsici on Black Pepper Plants

Published on: September 16, 2022

3.3K
A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.6K
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

科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 由于体积小,形状多样,背景复杂,胡病和害虫存在检测挑战.
  • 现有的物体检测方法与多种目标作斗争,并且在农业成像中缺乏噪声抑制.

研究的目的:

  • 在复杂的场景中开发一种强大的物体检测模型来检测胡病和害虫.
  • 创建第一个具有自然语言描述的多式病和害虫对象检测数据集 (PDD).

主要方法:

  • 提出了PepperNet模型,该模型将多式联网特征分解为明确的属性.
  • 采用细粒度的多模式属性对比学习来区分微妙的差异.
  • 使用了一个新的数据集 (PDD),集成图像和详细的文本描述.

主要成果:

  • 在对象检测方面获得了91.93%的平均平均精度 (mAP@0.5).
  • 达到了每秒121.8的高检测速度.
  • 通过可视化,证明了模型对噪声和阻塞的强度.

结论:

  • 在复杂的现实条件下,PepperNet提供了卓越的性能和稳定性来检测胡病和害虫.
  • 精细的多模式属性学习有效地将语言映射到视觉中,以进行精确的识别.
  • PDD数据集和PepperNet模型在农业中推进了自动化病虫害检测.