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

RNA-seq03:21

RNA-seq

9.7K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.7K

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

A multidimensional exploration of the immediate physiological and psychological restorativeness of nature education for children in Giant Panda National Park.

Frontiers in psychology·2026
Same author

Accuracy of new-generation intraocular lens formulas in IOL power calculation for cataract extraction combined with vitrectomy.

International ophthalmology·2026
Same author

"Lotus leaf meets nacre": a dual biomimetic approach to creating highly humidity-stable films with exceptional barrier properties for food packaging.

Food chemistry·2026
Same author

A generalized two-step floating building catchment area model for measuring EMS accessibility with in-building patient access.

BMC health services research·2026
Same author

PAD4-mediated citrullination of IGF2BP2 stabilizes MCM mRNAs to drive intrahepatic cholangiocarcinoma progression.

Journal of advanced research·2026
Same author

Dynamic frailty trajectories and risk of chronic obstructive pulmonary disease: a large-scale prospective cohort study.

BMC pulmonary medicine·2026

相关实验视频

Updated: May 21, 2025

Real-Time Detection of Reactive Oxygen Species Production in Immune Response in Rice with a Chemiluminescence Assay
05:44

Real-Time Detection of Reactive Oxygen Species Production in Immune Response in Rice with a Chemiluminescence Assay

Published on: November 25, 2022

3.5K

基于改进的YOLOv8的轻量级大米爆炸检测方法的研究.

Sixu Jin1, Qiang Cao1, Jinpeng Li1

  • 1College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China.

Pest management science
|March 22, 2025
PubMed
概括

这项研究引入了一种改进的YOLOv8模型,用于轻量级和精确的病检测. 改进后的模型显著降低了参数,并提高了精准农业的平均精度 (mAP).

关键词:
这就是YOLOv8的意义.深度学习是一种深度学习.图像识别功能 图像识别功能轻量级的轻量级的轻量级的轻量级的大米炸弹爆炸目标检测 目标检测 目标检测

更多相关视频

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone
07:36

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

1.5K
The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform
10:01

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

Published on: September 27, 2016

7.6K

相关实验视频

Last Updated: May 21, 2025

Real-Time Detection of Reactive Oxygen Species Production in Immune Response in Rice with a Chemiluminescence Assay
05:44

Real-Time Detection of Reactive Oxygen Species Production in Immune Response in Rice with a Chemiluminescence Assay

Published on: November 25, 2022

3.5K
Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone
07:36

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

1.5K
The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform
10:01

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

Published on: September 27, 2016

7.6K

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:

  • 及时发现大米疾病至关重要,以防止产量大幅下降和农民的经济损失.
  • 现有的方法经常在实时应用的准确性和模型效率方面扎.

研究的目的:

  • 开发一种轻量级且准确的病检测模型.
  • 改进特征提取,模型强度和界限框回归以提高性能.

主要方法:

  • 一个改进的YOLOv8架构,包括一个全维动态卷积 (ODConv) 模块.
  • 整合了WIoU (交叉与结合的顺序证据的加权插值) 机制,以优化界限框损失.
  • 使用高分辨率探测器头来改善小物体检测并减少网络参数.

主要成果:

  • 与YOLOv8n.相比,实现了66.6%的参数减少和61.9%的模型大小减少.
  • 证明了比较高的平均精度 (mAP) 超过了像Faster R-CNN,YOLOv5s,YOLOv6n,YOLOv7-tiny和YOLOv8n.n.等既有模型.
  • 检测性能显著改善,表明精度和效率提高.

结论:

  • YOLOv8-OW模型提供了一种有效的,轻量级的解决方案,用于实时检测米疾病.
  • 适合在资源有限的移动设备上部署,支持精准农业.
  • 促进农民准确和及时的疾病管理.