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    Home > Biochemistry News > Biotechnology News > Application of hyperspectral imaging technology to study on the quality of wolfberry varieties

    Application of hyperspectral imaging technology to study on the quality of wolfberry varieties

    • Last Update: 2022-08-30
    • Source: Internet
    • Author: User
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    Application Case 1: Differentiation of Lycium barbarum with different fruit sizes


    Application Case 2: Classification of Lycium barbarum from different origins based on hyperspectral images


    The study used a new hybrid convolutional neural network (New-Hybrid-CNN) architecture to make full use of pixel information to divide the wolfberry samples from each origin into 2500 3D patches, and randomly divide the wolfberry samples from different origins into 2,500 3D patches.
    For the training set (70%) and the test set (30%), 20% of the training set is randomly selected as the validation set
    .
    Then use a homogeneous 3D convolution architecture with 3×3×3 convolution kernels to extract the spectral-spatial joint information from the hyperspectral cubic information, and then use depthwise separable convolution (DSC) to learn the spatial information, combined with 3D convolution And the advantages of DSC, the deep spectral-spatial joint information is effectively extracted, making the architecture more lightweight
    .
    Comparing 3D-CNN, HybridSN and SVM with the proposed method under the same parameters, it is found that New-Hybrid-CNN shows a better classification effect (as shown in the left figure below), and requires the least number of parameters and the shortest time.
    and the most stable
    .



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    .

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