Maize variety identification method based on near infrared spectrum and information processing

A technology of near-infrared spectroscopy and corn, applied in color/spectral characteristic measurement, material analysis through optical means, measuring devices, etc., to achieve the effect of overcoming the low identification accuracy

Inactive Publication Date: 2010-09-01
INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
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Problems solved by technology

[0005] "Study on the Identification Method of Maize Varieties Based on Near-infrared Spectroscopy Technology and Artificial Neural Network" published by Chen Jian et al. in the Journal of Spectroscopy and Spectral Analysis, and "Based on Visible/Near Infrared Spectral Rapid Identi

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  • Maize variety identification method based on near infrared spectrum and information processing
  • Maize variety identification method based on near infrared spectrum and information processing
  • Maize variety identification method based on near infrared spectrum and information processing

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[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0026] The object of the present invention is to be divided into three steps and realize, figure 1 It is a flow chart of the corn variety identification method based on near-infrared spectrum and information processing provided by the present invention, and the method includes the following steps:

[0027] Step 1: Obtain spectral data;

[0028] Step 2: Perform normalized principal component analysis on the training sample set, and adjust the weight of the principal components according to the distribution of sample points on the main axis;

[0029] Step 3: Classify the classifier using the nearest neighbor classification method.

[0030] In the above step 1, the acquisition of spectral data is to use a Fourier transfor...

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Abstract

The invention discloses a maize variety identification method based on near infrared spectrum and information processing. The method comprises the following steps of: collecting spectrum data of maize seeds by adopting a Fourier transform diffuse reflectance near-infrared spectrometer; analyzing main components by adopting normalization according to the characteristic of spectrum, i.e. normalizing the quadratic sum of projections of sample points on each principal axis; adjusting the distribution of the sample points in a feature space; adjusting the weights of the main components according to the diffusion condition of the data projections on each principal axis; and finally, classifying by adopting a nearest neighbor classification method. Compared with the traditional chemical identification method, the method provided by the invention is efficient and rapid and needs no professional person to operate.

Description

technical field [0001] The invention relates to the technical field of corn variety identification, in particular to a corn variety identification method based on near-infrared spectrum and information processing. Background technique [0002] Existing identification methods for crop varieties include morphological methods, fluorescent scanning identification methods, chemical identification methods, and electrophoresis identification methods. Morphological methods require a long time for identification, and the accuracy is not high; fluorescence scanning identification method, chemical identification method and DNA molecular marker identification method have high identification accuracy, but it takes a long time, and the identification cost is high, and the process is cumbersome, so it is not suitable. Batch analysis and rapid identification of samples. [0003] Near-infrared spectroscopy refers to the absorption spectrum taken in the near-infrared, with a wavelength range...

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Application Information

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IPC IPC(8): G01N21/35G06N99/00G01N21/3563G01N21/359
Inventor 王徽蓉李卫军陈新亮
Owner INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
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