Hyperspectral imaging combined with dual-channel deep learning feature fusion model for fast and non-destructive recognition of brew wheat varieties

高光谱成像 人工智能 卷积神经网络 模式识别(心理学) RGB颜色模型 支持向量机 特征(语言学) 计算机科学 特征提取 人工神经网络 卷积(计算机科学) 计算机视觉 语言学 哲学
作者
Lipeng Han,Jianping Tian,Yuexiang Huang,Kangling He,Yan Liang,Xinjun Hu,Liangliang Xie,Haili Yang,Dan Huang
出处
期刊:Journal of Food Composition and Analysis [Elsevier BV]
卷期号:125: 105785-105785 被引量:9
标识
DOI:10.1016/j.jfca.2023.105785
摘要

In this study, a dual-channel deep learning feature fusion model (DLFM) was developed to process hyperspectral imaging data for the rapid and nondestructive identification of brewing wheat varieties. The DLFM model extracts spectral features using a one-dimensional convolution module and spatial image features from the RGB image using a two-dimensional convolution module. These features are then fused using a feature adaptive fusion module within the DLFM and input into the fully connected layer for variety recognition. Support vector machine (SVM), one-dimensional convolutional neural network (1DCNN), two-dimensional convolutional neural network (2DCNN), and DLFM were built, respectively. Among them, DLFM had the highest recognition accuracy, which was 99.18%, 97.30%, and 93.18% for the three-variety, four-variety, and five-variety wheat combinations, respectively. The average accuracies of all combinations were improved by 11.93%, 6.84%, 12.54%, and 2.39% for 1DCNN, 2DCNN, and 1DCNN of fused data, respectively, compared to SVM. The results show that hyperspectral imaging (HSI) combined with DLFM can realize fast and nondestructive identification of different brewing wheat varieties, providing a new method for variety identification of cereals.
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