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相关概念视频

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
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Applications of IR Spectroscopy: Overview01:11

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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Raman Spectroscopy: Overview01:20

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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相关实验视频

Updated: Jun 15, 2025

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
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通过可见光和近红外光谱学识别甘病,使用深度学习辅助的基于连续波波变换的光谱图.

Pauline Ong1, Jinbao Jian2, Xiuhua Li3

  • 1College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|August 24, 2024
PubMed
概括

本研究引入了一种新方法,用于检测甘疾病,使用可见和近红外 (Vis-NIR) 光谱学与卷积神经网络 (CNN) 和连续波波变换 (CWT) 结合起来. 这种方法显著提高了农民的疾病识别准确性.

关键词:
连续波形变换连续波形变换.深度学习是一种深度学习.接近红外的近红外.植物疾病 植物疾病甘是一种糖.

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科学领域:

  • 农业科学 农业科学
  • 频谱学是一种光谱学.
  • 机器学习 机器学习

背景情况:

  • 可见和近红外 (Vis-NIR) 光谱与化学测量是常见的植物疾病识别.
  • 提取相关的光谱特征仍然是一个重大挑战.

研究的目的:

  • 为了提高甘病的识别准确度.
  • 改进从Vis-NIR光谱 (380-1400 nm) 中提取光谱特征.
  • 将卷积神经网络 (CNN) 与连续波波变换 (CWT) 谱图结合起来.

主要方法:

  • 收集了130个甘叶样本.
  • 将1D CWT系数从Vis-NIR光谱转换为2D光谱图.
  • 使用CNN提取特征并将其集成到各种分类模型中 (决策树,KNN,PLSDA,RF).

主要成果:

  • 随机森林 (RF) 模型结合了谱图衍生特征,显示出卓越的性能.
  • 实现了0.9111的平均精度,0.9733的灵敏度,0.9791的特异性和0.9487.7的准确性.
  • 综合CNN-CWT方法有效地提取了光谱特征.

结论:

  • 开发的方法提供了一种非破坏性,快速和准确的方法来检测甘疾病.
  • 为农民提供关于作物健康管理的及时见解.
  • 旨在最大限度地减少作物损失和优化农业产量.