Abstract: 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 DBLS. 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.

Key words: spectroscopy, terahertz, broad learning system, mildewed wheat, image processing