Qualitative and quantitative classification of rice quality grades is achieved by constructing an effective coupling model of multi-source heterogeneous hyperspectral spectral information and image information. First, extract the spectral reflectance feature values of all pixels in the rice hyperspectral interest area and the rice hyperspectral image features output by the multi-scale filter under the optimal resolution configuration, and eliminate heterogeneous spectra through the heterogeneous feature dimensionless quantization function. Features and image feature dimensions influence, construct an effective coupling mechanism in the high-dimensional Hilbert space, eliminate the impact of rice nonlinear hyperspectral multi-source quality level information heterogeneous characteristics, in the high-dimensional coupling space based on the measurement between samples to multiple Classification of source heterogeneous rice hyperspectral nonlinear characteristic quality grade information. The results show that the multi-source heterogeneous rice nonlinear hyperspectral information coupling identification model obtains 84.5% training accuracy and 82.2% test accuracy. It is proposed that the multi-source heterogeneous rice quality grade nonlinear hyperspectral information coupling identification model has better performance than The traditional classification model directly uses single source feature information. Shows that the proposed model can be used to determine rice quality grades more accurately

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Original research was done by Lin Ping, Jiang Shanchao, Chen Yongming, Fu Xiaorong, Gu Chunlei

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