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Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features
Alternative Title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.
Department光谱成像技术实验室
2018-05-01
Source PublicationSPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
Volume38Issue:5Pages:1514-1519
Contribution Rank1
Abstract

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.

SubtypeArticle
KeywordVis-nirs Macadamia Nut Deep Learning Convolutional Neural Network (Cnn) Quality Identification
Subject AreaSpectroscopy
WOS HeadingsScience & Technology ; Technology
DOI10.3964/j.issn.1000-0593(2018)05-1514-06
Indexed BySCI ; EI ; CSCD
WOS KeywordSpectroscopy ; Classification
Language中文
WOS Research AreaSpectroscopy
WOS SubjectSpectroscopy
WOS IDWOS:000432512000030
CSCD IDCSCD:6243807
EI Accession Number20183905879830
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30313
Collection光谱成像技术实验室
Corresponding AuthorHu, BL (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Shaanxi, Peoples R China.
Affiliation1.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
Recommended Citation
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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