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

8.5K
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.
8.5K

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

Effects of Medium-Chain Versus Medium- and Long-Chain Triglycerides, Combined with Carotenoids, in a High-Fat Diet on Obese Mice.

Foods (Basel, Switzerland)·2026
Same author

Study on the Performance of Modified Asphalt Mixture Incorporating MSWI Bottom Ash.

Materials (Basel, Switzerland)·2026
Same author

SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery.

Plants (Basel, Switzerland)·2026
Same author

Preparation of solid-state emissive carbon quantum dots and their integration into electroluminescent light-emitting diodes.

Nature protocols·2026
Same author

Unlocking Interfacial Binder Chemistry for Efficient Li<sup>+</sup> Desolvation in 3C-Rate Lithium Metal Pouch Cells.

Angewandte Chemie (International ed. in English)·2026
Same author

From disordered dots to coherent pixels: superlattice ordering enables high-performance perovskite LEDs.

Science bulletin·2026

相关实验视频

Updated: Jun 26, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
15:25

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

26.4K

使用LAFANet的高精度番茄叶病图像文本检索方法

Jiaxin Xu1, Hongliang Zhou1, Yufan Hu1

  • 1College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.

Plants (Basel, Switzerland)
|May 11, 2024
PubMed
概括

这项研究介绍了LAFANet,这是一种新的图像文本检索方法,用于在智能农业中诊断番茄叶病. 通过融合视觉和文本数据,LAFANet提高了诊断准确性,提高了番茄产量和质量.

关键词:
这就是为什么AR AR AR AR.在 FNE-ANS 中.拉法尼特 (LAFANet) 是一个在线网络.在LFA,LFA,LFA.TLDITRD 是一个很大的领域.这是一个跨模式的跨模式.图像-文本检索 图像-文本检索

更多相关视频

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

28.4K
A Simple Method for Imaging Arabidopsis Leaves Using Perfluorodecalin as an Infiltrative Imaging Medium
05:19

A Simple Method for Imaging Arabidopsis Leaves Using Perfluorodecalin as an Infiltrative Imaging Medium

Published on: January 16, 2012

21.7K

相关实验视频

Last Updated: Jun 26, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
15:25

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

26.4K
LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

28.4K
A Simple Method for Imaging Arabidopsis Leaves Using Perfluorodecalin as an Infiltrative Imaging Medium
05:19

A Simple Method for Imaging Arabidopsis Leaves Using Perfluorodecalin as an Infiltrative Imaging Medium

Published on: January 16, 2012

21.7K

科学领域:

  • 智能农业 智能农业
  • 植物病理学 植物病理学
  • 计算机视觉 计算机视觉
  • 自然语言处理自然语言处理.

背景情况:

  • 有效的番茄叶病控制对于智能农业至关重要.
  • 目前的诊断方法缺乏全面的多式联运分析.
  • 图像文本检索提供了改善诊断证据的潜力.

研究的目的:

  • 开发一种有效的图像-文本检索方法来治疗番茄叶病.
  • 引入多式联络数据集用于番茄叶病检索.
  • 提高诊断的准确性和支持农业从业人员.

主要方法:

  • 创建了番茄叶病图像文本检索数据集 (TLDITRD).
  • 使用视觉变压器 (ViT) 和BERT进行特征提取.
  • 建议用于特征融合的可学习的融合注意力 (LFA).
  • 实施虚假负面消除-对抗性负面选择 (FNE-ANS) 和对抗性规范化 (AR).

主要成果:

  • 在TLDITRD数据集上,LAFANet在现有模型上表现优越.
  • 实现了顶级-1,顶级-5和顶级-10的检索率,分别为83.3%,90.0%和80.3%,93.7%和96.3%.
  • 验证了LFA,FNE-ANS和AR在提高检索准确度方面的有效性.

结论:

  • LAFANet提供了一个强大的框架,用于使用多式联络数据检索番茄叶病.
  • 拟议的方法为智能农业诊断提供了重要的技术进步.
  • 拉法网支持加强决策,以确保番茄的质量和产量.