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Related Concept Videos

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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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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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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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Related Experiment Video

Updated: Jun 15, 2025

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Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous

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
Summary
This summary is machine-generated.

This study introduces a new method for detecting sugarcane diseases using visible and near-infrared (Vis-NIR) spectroscopy combined with convolutional neural networks (CNNs) and continuous wavelet transform (CWT). This approach significantly improves disease recognition accuracy for farmers.

Keywords:
Continuous wavelet transformDeep learningNear-infraredPlant diseaseSugarcane

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Machine Learning

Background:

  • Visible and near-infrared (Vis-NIR) spectroscopy with chemometrics is common for plant disease identification.
  • Extracting relevant spectral features remains a significant challenge.

Purpose of the Study:

  • To enhance sugarcane disease recognition accuracy.
  • To improve spectral feature extraction from Vis-NIR spectra (380-1400 nm).
  • To combine convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms.

Main Methods:

  • Collected 130 sugarcane leaf samples.
  • Transformed 1D CWT coefficients from Vis-NIR spectra into 2D spectrograms.
  • Extracted features using CNN and integrated them into various classification models (Decision Tree, KNN, PLSDA, RF).

Main Results:

  • The Random Forest (RF) model incorporating spectrogram-derived features showed superior performance.
  • Achieved an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487.
  • The combined CNN-CWT approach effectively extracted spectral features.

Conclusions:

  • The developed method offers a non-destructive, rapid, and accurate approach for sugarcane disease detection.
  • Provides farmers with timely insights for crop health management.
  • Aims to minimize crop loss and optimize agricultural yields.