Jove
Visualize
联系我们

相关概念视频

Light Acquisition02:16

Light Acquisition

8.6K
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.
8.6K

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

Gated recurrent unit model for forecasting greenhouse gas concentrations with uncertainty quantification.

Frontiers in artificial intelligence·2026
Same author

Adaptive class-aware feature selection for high-dimensional and imbalanced multi-class network intrusion detection.

Frontiers in big data·2026
Same author

A labelled dataset of healthy and diseased common bean (<i>Phaseolus vulgaris</i>) from Tanzania.

Data in brief·2026
Same author

Enhanced SQL injection detection using chi-square feature selection and machine learning classifiers.

Frontiers in big data·2025
Same author

Adaptive Antenna for Maritime LoRaWAN: A Systematic Review on Performance, Energy Efficiency, and Environmental Resilience.

Sensors (Basel, Switzerland)·2025
Same author

Irish potato imagery dataset for detection of early and late blight diseases.

Data in brief·2025
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: Sep 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

491

使用快速渐变标志法训练的视觉变换器提高常见豆类疾病的检测能力

Upendo Mwaibale1, Neema Mduma1, Hudson Laizer2

  • 1Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania.

Frontiers in artificial intelligence
|August 22, 2025
PubMed
概括

在坦桑尼亚早期发现常见的豆类疾病至关重要. 使用视觉转换器 (ViT) 和对抗训练的新深度学习模型实现了99.4%的准确性,用于农场中基于移动的强大疾病检测.

关键词:
快速渐变标志方法视觉变压器 (ViT)敌对的攻击豆糖豆子生深度学习

更多相关视频

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.9K
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

1.6K

相关实验视频

Last Updated: Sep 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

491
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.9K
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

1.6K

科学领域:

  • 农业科学
  • 计算机科学
  • 植物病理学

背景情况:

  • 坦桑尼亚的豆类生产面临诸如豆和豆腐等疾病的严重威胁.
  • 有效的疾病管理取决于及时准确的早期检测系统.

研究的目的:

  • 开发一个强大的深度学习模型来早期发现常见的豆类疾病.
  • 提高对现实农场条件的模型可靠性,特别是在资源有限的环境中.

主要方法:

  • 一个基于视觉转换器 (ViT) 的深度学习模型被开发并通过对抗训练来增强.
  • 使用几何,颜色和FGSM扰动来模拟场变性的100,000个注释图像的数据集.
  • 该模型使用转移学习进行了微调,并通过交叉验证进行了验证.

主要成果:

  • 这种对抗训练显著提高了模型对抗图像干扰的稳定性.
  • 微调的ViT模型在疾病检测方面达到99.4%的高精度.
  • 该研究表明该模型对基于移动的植物疾病诊断的有效性.

结论:

  • 整合对抗性强度有效提高植物疾病检测的深度学习模型的可靠性.
  • 开发的模型有望在资源有限的农业环境中在基于移动的疾病诊断中得到实际应用.
  • 这种方法有助于减轻农作物损失,改善依赖常豆生产的地区的粮食安全.