【摘要】 小麦质量安全是粮食安全的重要组成部分。传统的小麦霉变籽粒识别检测方法需要复杂的处理步骤,耗时较长且特征提取能力较差,易造成图像有效信息的丢失,导致小麦霉变籽粒识别检测效果不佳。为解决上述问题,提出了一种基于去噪宽度学习(D-BLS)的霉变小麦太赫兹光谱图像识别方法。该方法对传统宽度学习(BLS)算法进行了改进,通过引入去噪卷积神经网络(DnCNN)模块,构建D-BLS霉变小麦分类识别模型,以增强图像质量,提高霉变小麦太赫兹光谱图像的识别精度。初步研究表明, D-BLS在识别准确率方面优于传统BLS算法,识别准确率达到93.13%。进一步使用支持向量机(SVM)、后向传播神经网络(BPNN)、卷积神经网络(CNN)与D-BLS进行建模对比。研究结果表明, D-BLS网络的分类准确率分别比SVM、BPNN和CNN高出了13.83%、7.79%和3.96%。因此, DBLS能够为小麦发霉早期鉴别提供一种新方法。更多还原

【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.更多还原