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Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features
其他题名基于卷积神经网络与光谱特征的夏威夷果品质鉴定研究
Du Jian1,2; Hu Bing-liang1; Liu Yong-zheng1; Wei Cui-yu1; Zhang Geng1; Tang Xing-jia1; Hu, BL (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Shaanxi, Peoples R China.
作者部门光谱成像技术实验室
2018-05-01
发表期刊SPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
卷号38期号:5页码:1514-1519
产权排序1
摘要

Macadamia nut is easy to spoil after being stripped off because of the high level of oil in it. Most of the existing traditional methods are destructive which are difficult to satisfy the demand of non-destructive detection. As one of the widely used deep learning models, convolutional neural network (CNN) has stronger capabilities of feature extraction and model formulation than shallow learning methods and great potential for the application of spectral data. We studied suitable CNN architecture to extract spectral features of Macadamia based on Vis-NIRS analysis, and proposed an efficient non-destructive method to identify the quality of Macadamia. At first, we took three kinds of macadamia nut with different qualities (including better nut, worse nut and moldy nut) as the research object and analyzed the spectral information in the wavelength range of 500 similar to 2 100 nm. We introduced the concept of whitening in data preprocessing to strengthen the correlation difference. In the process of model training, we divided the sample into training set and prediction set randomly and then discussed the effects of different structure parameters, such as the number of convolution layer, size of convolution kernel, pooling type, number of neuron in full connection layer and activation function. We applied ReLU and Dropout to prevent over-fitting caused by lack of data. At last, through the analysis of the classification accuracy and computational efficiency, a CNN model of 6-layer structure was established: input layer-convolution layer-pooling layer-full connection layer(including 200 neurons)-full connection layer(including 100 neurons)-output layer. The results show that the final classification accuracy of the calibration set and prediction set reached 100%. This improved CNN model can fully learn the spectral features of macadamia and classify effectively. The combination of the deep learning theory and the spectral analysis method can identify the quality of macadamia accurately, and provide a new idea for the efficient, non-destructive, real-time, online detection of macadamia and other nuts.

文章类型Article
关键词Vis-nirs Macadamia Nut Deep Learning Convolutional Neural Network (Cnn) Quality Identification
学科领域Spectroscopy
WOS标题词Science & Technology ; Technology
DOI10.3964/j.issn.1000-0593(2018)05-1514-06
收录类别SCI ; EI ; CSCD
关键词[WOS]Spectroscopy ; Classification
语种中文
WOS研究方向Spectroscopy
WOS类目Spectroscopy
WOS记录号WOS:000432512000030
CSCD记录号CSCD:6243807
EI入藏号20183905879830
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30313
专题光谱成像技术实验室
通讯作者Hu, BL (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Shaanxi, Peoples R China.
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Du Jian,Hu Bing-liang,Liu Yong-zheng,et al. Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2018,38(5):1514-1519.
APA Du Jian.,Hu Bing-liang.,Liu Yong-zheng.,Wei Cui-yu.,Zhang Geng.,...&Hu, BL .(2018).Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features.SPECTROSCOPY AND SPECTRAL ANALYSIS,38(5),1514-1519.
MLA Du Jian,et al."Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features".SPECTROSCOPY AND SPECTRAL ANALYSIS 38.5(2018):1514-1519.
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