Wheat flour gluten degree detection method based on cascade forest and convolutional neural network

Abstract

The invention particularly relates to a wheat flour gluten degree detection method based on a cascade forest and a convolutional neural network. The wheat flour gluten degree detection method comprises the following steps: A, selecting wheat flour with known gluten degree as a sample, and dividing the sample into a training set and a test set; B, acquiring a hyperspectral image; C, extracting a single-waveband graph; D, obtaining image features; E, performing map fusion on the characteristic wavelength and the image features, taking the fused characteristic wavelength and image features as characteristic values, taking a gluten degree label as a result, and substituting the result into the cascade forest model for training to obtain a wheat flour gluten degree identification model; and F,substituting the test set into the trained wheat flour gluten identification model for testing to obtain a predicted gluten category and category accuracy. Spectral features are obtained through hyperspectral data to establish the wheat flour gluten control model, then input data are compared and analyzed through the model, the gluten of wheat flour is rapidly and nondestructively recognized, whether the gluten of the wheat flour meets the requirements of the manufacturing process or not is judged, and the detection method is high in applicability, high in detection precision and high in practicability, and realizes wheat flour gluten degree detection in a nondestructive manner.

Description

technical field [0001] The invention relates to the technical field of agricultural product quality detection, in particular to a method for detecting gluten of wheat flour based on cascade forest and convolutional neural network. Background technique [0002] Wheat flour is one of the essential foods on the Chinese table. Wheat flour can be used to make pasta such as steamed buns, cakes, and gluten. However, for different pasta, the requirements for gluten are also different. Once the gluten of wheat flour does not meet the standard, it will greatly increase the difficulty of making pasta and affect the taste of pasta. With the improvement of living standards, consumers began to pay attention to whether the gluten of wheat flour meets the requirements when purchasing wheat flour. At present, the situation that the gluten of wheat flour is not strictly controlled is constantly appearing in sales, and these phenomena destroy the consumption experience of consumers. Due to ...

AI Technical Summary

Problems solved by technology

Due to the small difference in appearance, relying only on human vision and smell, the subjectivity is too strong, and it is easy to cause misjudgment, which cannot meet the above detection requirements.

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