Jove
Visualize
联系我们

相关概念视频

Light Acquisition02:16

Light Acquisition

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.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.

Network (Bristol, England)·2024
Same journal

Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence.

Network (Bristol, England)·2026
Same journal

Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach.

Network (Bristol, England)·2025
Same journal

A novel lung cancer diagnosis model using hybrid convolution (2D/3D)-based adaptive DenseUnet with attention mechanism.

Network (Bristol, England)·2025
Same journal

Hybrid optimization enabled Eff-FDMNet for Parkinson's disease detection and classification in federated learning.

Network (Bristol, England)·2025
Same journal

AI-driven plant disease detection with tailored convolutional neural network.

Network (Bristol, England)·2025
Same journal

Layer modified residual Unet++ for speech enhancement using Aquila Black widow optimizer algorithm.

Network (Bristol, England)·2025
查看所有相关文章
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

相关实验视频

Updated: Jun 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

使用强大的编码-解码级联深度学习模型进行植物叶子感染点细分.

David Femi1, Manapakkam Anandan Mukunthan1

  • 1Research Scholar, Professor Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.

Network (Bristol, England)
|November 30, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度编码解码级联网络 (DEDCNet) 精确地细分了病变的叶子斑点,改善了植物疾病的诊断. 这种先进的模型通过准确识别和分类各种叶子感染来提高农业产量.

关键词:
农作物叶病的分类 植物叶病的分类一个金字塔的聚合方式.在SVM中,SVM是SVM.深度学习是一种深度学习.编码器-解码器网络的编码器-解码器网络.多尺度扩展卷积核多尺度扩展卷积核细分化 细分化的细分化

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

784
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

559

相关实验视频

Last Updated: Jun 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

784
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

559

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:

  • 准确的叶病诊断对于农业生产率和降低成本至关重要.
  • 病变的叶子斑点的不准确细分可能导致植物疾病的错误分类.
  • 疾病特征和尺寸的重叠对精确的细分构成了挑战.

研究的目的:

  • 提出一种新的深度编码解码级联网络 (DEDCNet),用于精确的叶子图像分割.
  • 为了准确地细分病变的叶子斑点,并区分相似的植物疾病.
  • 提高叶病分类和诊断的整体准确性.

主要方法:

  • 开发了一个DEDCNet模型,包括一个感染点识别网络 (ISRN) 和一个感染点细分网络 (ISSN).
  • ISRN将级联卷积神经网络 (CNN) 与特征金字塔聚合集成,用于感染点的识别.
  • 为了实现精确的细分,ISSN采用了一个编码器-解码器架构,具有多尺度扩展卷积.
  • 使用预先学习的CNN来提取纹理特征,以及用于疾病分类的支持矢量机 (SVM).

主要成果:

  • 在高精度,回忆和F-score的贝特尔叶图像数据集上实现了94.89%的准确性.
  • 在贝特利叶数据集中,低低的低细分率 (6.2%) 和过度细分率 (2.8%) 已被证明.
  • 在PlantVillage数据集上达到96.5%的准确性,优于现有模型.
  • 在不到0.1秒的时间内,在各自的数据集上实现了0.9822和0.9834的子系数.

结论:

  • DEDCNet模型显著提高了叶病细分和分类准确度.
  • 与现有的植物疾病分析模型相比,拟议的方法提供了更高的效率.
  • 准确的细分对于可靠的疾病诊断至关重要,有助于改善农业成果.