Spectral discrimination of tea plant varieties by statistical, machine learning and spectral similarity methods

No abstract is available for this paper.

• Publication year

  2014

• Venue

  Workshop on Hyperspectral Image and Signal Processing

• Publication date

  2014-06-01

• Fields of study

  Agricultural and Food Sciences, Mathematics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/WHISPERS.2014.8077526
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Normalized Spectral Similarity Score ( ${\hbox{NS}}^{3}$) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification

  2011cited by this paper
• Spectral Discrimination of Grape Varieties and a Search for Terroir Effects Using Remote Sensing

  2011cited by this paper
• Vine variety discrimination with airborne imaging spectroscopy

  2007cited by this paper
• Discrimination of transgenic tomatoes based on visible/near-infrared spectra.

  2007cited by this paper
• Fast Discrimination of Apple Varieties Using Vis/NIR Spectroscopy

  2007cited by this paper
• Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model

  2007cited by this paper
• Potential of Artificial Neural Networks in Varietal Identification using Morphometry of Wheat Grains

  2006cited by this paper
• Evaluation of Neural-network Classifiers for Weed Species Discrimination

  2005cited by this paper
• Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data

  2005cited by this paper
• Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales

  2005cited by this paper
• A new method for non-parametric multivariate analysis of variance in ecology

  2001influential reference
• Smoothing and differentiation of data by simplified least square procedure.

  1964cited by this paper

• No citing papers are available for this paper.