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

8.6K
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

Latent profiles of kinesiophobia and their associations with exercise self-efficacy, diabetes distress, and fall risk in older adults with type 2 diabetes: a cross-sectional study.

BMC geriatrics·2026
Same author

An Intelligent Machine-Learning-Driven Metalloporphyrin Covalent Organic Framework Sensor Array for Pesticide Discrimination.

Analytical chemistry·2026
Same author

Passive source localization with a horizontal line array in the shadow zone of the deep water.

The Journal of the Acoustical Society of America·2026
Same author

Effect of renin-angiotensin system inhibition on left ventricular mass regression after transcatheter aortic valve replacement: a randomised controlled trial.

Heart (British Cardiac Society)·2026
Same author

Isolinderalactone suppresses the progression of cholangiocarcinoma by modulating the CARMA1-BCL10-MALT1 signalosome.

The Journal of biological chemistry·2026
Same author

An expert-guided multi-agent reinforcement learning framework with balanced exploration for uncontrolled intersections.

ISA transactions·2026

相关实验视频

Updated: Sep 18, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

1.1K

玉米种子高光谱图像的分类基于可变深度的卷积核.

Yating Hu1, Hongchen Zhang1,2, Changming Li2

  • 1College of Information Technology, Jilin Agricultural University, Changchun, China.

Frontiers in plant science
|June 23, 2025
PubMed
概括

一个新的可变深度卷积神经网络 (VD-CNN) 通过分析光谱和纹理特征来准确地分类玉米种子. 这种机器学习方法提高了种子分类的准确性,并为农业应用提供了强大的框架.

关键词:
这是一个3D卷积内核.在美国,CNN是CNN.玉米玉米玉米玉米玉米超光谱图像的使用具有可变深度的卷积核.品种识别 品种识别

更多相关视频

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K
Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
06:21

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

8.9K

相关实验视频

Last Updated: Sep 18, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

1.1K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K
Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
06:21

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

8.9K

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:

  • 精确的种子分类对于生殖细胞利用和育种效率至关重要.
  • 手动分类是劳动密集型和容易出错的.
  • 卷积神经网络 (CNN) 是有前途的,但难以有效地整合光谱和纹理数据.

研究的目的:

  • 开发一种新的CNN架构,用于增强高光谱种子分类.
  • 从高光谱图像中同时提取光谱和纹理特征.
  • 提高玉米种子分类的准确性和稳定性.

主要方法:

  • 提出了一个可变深度卷积神经网络 (VD-CNN) 架构.
  • 采用自适应内核深度调制用于光谱特征提取.
  • 使用层次的卷积运算来捕获纹理图案.
  • 在玉米和大米种子数据集上训练和评估了12个具有不同核心深度的模型.

主要成果:

  • VD-CNN在15个内核深度实现了最佳性能,达到98.65%的培训和96.97%的玉米种子测试准确度.
  • 在公开的米种子数据集上表现出优异的概括性,比基准数据高出3.14%.
  • 在不同的作物物种和成像条件中验证了模型的稳定性.

结论:

  • VD-CNN有效地整合了光谱和纹理信息,用于高级超光谱种子分类.
  • 拟议的架构为种子分类和其他农业高光谱成像应用提供了重大进展.
  • 强调了适应性深度学习模型在精准农业中的潜力.