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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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Related Experiment Video

Updated: Jun 19, 2026

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton
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Published on: April 28, 2017

[Cotton identification and extraction using near infrared sensor and object-oriented spectral segmentation

Jin-Song Deng1, Yuan-Yuan Shi, Li-Su Chen

  • 1Institute of Remote Sensing & Information Technique, Zhejiang University, Hangzhou 310029, China. jsong_deng@zju.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|October 6, 2009
PubMed
Summary

This study introduces an object-oriented segmentation technique for precise cotton identification in precision agriculture. The method achieves 96.33% accuracy, enabling automated crop management.

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Computer Vision

Context:

  • Precision agriculture relies on accurate crop identification for scientific management.
  • Traditional pixel-based methods struggle with complex image processing and objective identification.
  • High-resolution visible-near infrared imagery is crucial for detailed crop analysis.

Purpose:

  • To develop and evaluate an object-oriented segmentation technique for precise cotton identification.
  • To overcome limitations of traditional pixel-based methods in crop identification.
  • To integrate spectral, shape, and topological features for accurate crop classification.

Summary:

  • Visible-near infrared images of cotton were acquired using a high-resolution sensor.
  • An object-oriented segmentation technique generated image objects with spatial/spectral features.
  • A nearest neighbor classifier utilized these features for precise cotton identification, achieving 96.33% overall accuracy and a KAPPA coefficient of 0.9267.

Impact:

  • The developed method provides a reliable foundation for scientific crop management in precision agriculture.
  • It meets the demands for automatic management and decision-making in modern farming.
  • This approach enhances the efficiency and accuracy of crop identification systems.