Chinese Journal of Quantum Electronics, Volume. 40, Issue 3, 360(2023)

Identification of wheat mold using terahertz images based on Broad Learning System

GE Hongyi1...2, WANG Fei1,2, JIANG Yuying1,3,*, LI Li1,2, ZHANG Yuan1,2,**, and JIA Keke12 |Show fewer author(s)
Author Affiliations
  • 1Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology,Zhengzhou 450001, China
  • 2College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
  • 3School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China
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    The quality and safety of wheat is an important part of food safety. The traditional identification and detection method of moldy wheat seed requires complex processing steps, which is time-consuming and has poor feature extraction capability, and is prone to the loss of effective image information, resulting in poor wheat moldy seed identification detection. To solve the above problems, a terahertz spectral image recognition method for moldy wheat based on denoising convolutional neural network-broad learning system (D-BLS) is proposed in this paper. The method improves the traditional broad learning system (BLS) algorithm and constructs a D-BLS moldy wheat classification and recognition model by introducing a denoising convolutional neural network (DnCNN) denoising network to enhance image quality and improve the recognition accuracy of moldy wheat terahertz spectral images. The results show that D-BLS outperforms the traditional BLS algorithm in terms of recognition accuracy, with a recognition accuracy of 93.13%. Fruthermore, support vector machine (SVM), back propagation neural network (BPNN), convolutional neural network (CNN) are used for modeling to compare with D-BLS. The experimental results show that the classification accuracy of the D-BLS network is 13.83%, 7.79% and 3.96% higher than that of SVM, BPNN and CNN, respectively. Therefore, it is believed that the proposed D-BLS algorithm can provide a new effective method for early identification of wheat mold.

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    Hongyi GE, Fei WANG, Yuying JIANG, Li LI, Yuan ZHANG, Keke JIA. Identification of wheat mold using terahertz images based on Broad Learning System[J]. Chinese Journal of Quantum Electronics, 2023, 40(3): 360

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    Paper Information

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    Received: Oct. 10, 2022

    Accepted: --

    Published Online: Jun. 30, 2023

    The Author Email: JIANG Yuying (jiangyuying11@163.com), ZHANG Yuan (zy_haut@163.com)

    DOI:10.3969/j.issn.1007-5461.2023.03.007

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