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

8.4K
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.
8.4K

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

Pan-cancer analysis and experimental validation reveal UTP4 as a novel biomarker for gastric cancer.

Molecular and clinical oncology·2026
Same author

DiPTAC: A degradation platform <i>via</i> directly targeting proteasome.

Acta pharmaceutica Sinica. B·2025
Same author

Ultraviolet spectral transfer based on a convolutional variational autoencoder model for detecting chemical oxygen demand in rivers.

Analytical methods : advancing methods and applications·2025
Same author

A rapid diagnostic technique based on metabolomics to differentiate between preeclampsia (PE) and chronic kidney disease (CKD) using maternal urine.

European journal of obstetrics & gynecology and reproductive biology: X·2024
Same author

RNA modifications in cancer immune therapy: regulators of immune cells and immune checkpoints.

Frontiers in immunology·2024
Same author

Monitoring nonlinear large gradient subsidence in mining areas through SBAS-InSAR with PUNet and Weibull model fusion.

Environmental science and pollution research international·2024

相关实验视频

Updated: Jun 1, 2025

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.1K

基于多光谱成像的果伤的检测,使用细分网络和分类模型.

Yanru Fang1, Hongyi Bai1,2, Laijun Sun1,2

  • 1College of Electronics and Engineering, Heilongjiang University, Harbin, China.

Journal of food science
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用多光谱成像的深度学习方法,以准确检测果伤水平和时间. 该方法显著改善了伤检测和分类,为水果行业提供了一个新的工具.

关键词:
伤水平和果的时间.伤的地区提取提取改进了DeepLabV3+的使用情况.改进了DenseNet121的使用情况.多光谱成像技术多光谱成像技术

更多相关视频

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

698
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

相关实验视频

Last Updated: Jun 1, 2025

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.1K
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

698
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

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:

  • 伤会对果的外观,营养价值和营销能力产生负面影响,导致经济损失.
  • 准确及时检测伤对于果行业的质量控制至关重要.

研究的目的:

  • 开发和验证一种用于精确检测果伤水平和时间的新方法.
  • 通过深度学习和多光谱成像来提高伤细分和分类的准确性.

主要方法:

  • 一个自行设计的多谱成像系统与一个改进的DeepLabV3+模型相结合,用于伤细分.
  • 使用深度可分离的卷积,高效的通道注意力和焦点损失来提高细分精度.
  • 来自伤区域的光谱数据使用改进的DenseNet121进行了分析,用于伤水平和时间识别,并结合了共弦和注意力机制.

主要成果:

  • 改进的DeepLabV3+在伤细分方面实现了高交叉超过联合 (IoU) 得分 (高达95.5%) 和F1得分 (高达97.5%).
  • 增强的DenseNet121模型在识别伤水平 (精度高达99.5%) 和伤时间 (精度高达99.3%) 中表现出卓越的性能.

结论:

  • 提出的基于深度学习的多谱成像方法为检测果伤水平和时间提供了高度准确和有效的解决方案.
  • 这项技术有可能显著减少经济损失,并改善果生产和供应链的质量控制.