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Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks

文献类型： 外文期刊

作者： Shen, Yin 1 ; Yin, Yanxin 2 ; Li, Bin 2 ; Zhao, Chunjiang 1 ; Li, Guanglin 1 ;

作者机构： 1.Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China

2.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： THz spectral imaging; Convolutional neural network; Impurity detection; Loss function; Confusion matrix

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2021 年 181 卷

页码：

收录情况： SCI

摘要： The aim of this work was to propose a method to rapidly and effectively detect impurities contained in wheat based on a combination of terahertz spectral imaging and a convolutional neural network. First, the spectral characteristics of wheat, wheat husk, wheat straw, wheat leaf, wheat grain, weed, and ladybugs within the range of 0.2-1.6 THz were studied using the THz spectral imaging, and the corresponding frequency-domain spectra were obtained using Fourier transformation. The absorption coefficient and refractive index were then calculated. THz pseudo-color imaging was conducted next on wheat and its impurities according to the principle of maximum frequency-domain imaging, and a novel Wheat-V2 convolutional neutral network (CNN) was designed to extract the data and information regarding spectral imaging features. Finally, the designed Wheat-V2 model was compared with the ResNet-V2_50 and ResNet-V2_101 models under the same conditions. In addition, the loss function and confusion matrix indicators were used to evaluate the experimental results. The results show that the designed Wheat-V2 model can effectively recognize the impurities in wheat images, with a recognition accuracy of 97.56% and 98.58% for the verification sets Top_l and Top_5, respectively. In addition, the designed Wheat-V2 model achieved an average Fl-score of 97.83% in terms of image recognition of various impurities, which is higher than that achieved by conventional models, i.e. ResNet-V2_50 and ResNet-V2_101. This indicates that the method combining THz spectral imaging and CNN can be used for the detection of impurities in wheat. In addition, the results also indicate the potential of application of CNN in THz imaging detection of impurities in wheat, providing a nondestructive testing method for the recognition of impurities in other grains.

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