Tobacco leaf mildew rapid identification method based on wavelet transform-random forest algorithm

Abstract

The invention provides a tobacco leaf mildew rapid identification method based on a wavelet transform-random forest algorithm. The method comprises the following steps: collecting a tobacco leaf sample; respectively collecting the near infrared spectrum of each tobacco leaf sample as the spectral information of each tobacco leaf sample; preprocessing the spectral information by using discrete wavelet transform, and solving and obtaining a wavelet coefficient; and identifying the mildew degree of the tobacco leaves from the wavelet coefficients by utilizing a random forest algorithm to finish the identification of the mildew of the tobacco leaves. According to the tobacco leaf mildew rapid identification method based on the wavelet transform-random forest algorithm provided by the invention, a rapid identification method of tobacco leaf samples with different mildew degrees is established by using a near infrared spectrum method, and a basis is provided for early warning of tobacco leafmildew; spectral data is processed through wavelet transformation, spectral variables are determined, a random forest recognition model is established, the recognition rate and the forecasting rate are high, and effective recognition and accurate prediction of the mildew degree of the tobacco leaf sample are achieved.

Description

technical field [0001] The invention relates to the technical field of tobacco mildew identification, and more specifically, relates to a rapid identification method for tobacco mildew based on wavelet transform-random forest algorithm. Background technique [0002] Tobacco leaf is a special leaf plant, and tobacco storage is an extremely important link. Tobacco leaves grown in the field are harvested, roasted, purchased, transported, threshed and re-roasted, and alcoholized before entering the silk-making workshop of the cigarette factory and rolling them into cigarettes. It takes at least 1 to 2 years from tobacco leaves to cigarettes. Mold is the most widely distributed fungal microorganism in nature. As long as it meets the temperature and humidity conditions suitable for its growth and reproduction, it will grow and reproduce rapidly, causing mildew and deterioration of tobacco leaves. Therefore, research on tobacco mildew prevention has important economic value and a...

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Problems solved by technology

Based on the change of ergosterol content in tobacco leaves, Zhou Jiyue[10] established a pattern recognition model for the mildew process of tobacco leaves by using near-infrared spectroscopy combined with chemometric methods. Changes in the content of ergosterol in the medium can better exclude the influence of differences in tobacco leaf regions, parts and grades on the model, but because of this, there are bound to be limitations in the transfer and application of the model. , need to rebuild the model

Yang Lei [11] collected near-infrared spectra of tobacco leaves in the range of 780nm to 2500nm with a near-infrared spectrometer, and established a least squares-discriminant analysis model, which was applied to the prediction of moldy tobacco leaves, but the model only Judgment is made between mildewed tobacco samples and normal tobacco samples, without considering the prediction of tobacco leaves that are close to mildew

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