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Article

Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

by 1,2, 1,2, 3, 1,2, 1,2 and 1,2,*
1
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
3
College of Information Science and Technology, Shihezi University, Shihezi 832000, China
*
Author to whom correspondence should be addressed.
Molecules 2019, 24(18), 3268; https://doi.org/10.3390/molecules24183268
Received: 28 July 2019 / Revised: 2 September 2019 / Accepted: 6 September 2019 / Published: 7 September 2019
Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties. View Full-Text
Keywords: near-infrared hyperspectral imaging; cotton seed; convolution neural network; residual network; classifier near-infrared hyperspectral imaging; cotton seed; convolution neural network; residual network; classifier
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MDPI and ACS Style

Zhu, S.; Zhou, L.; Gao, P.; Bao, Y.; He, Y.; Feng, L. Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties. Molecules 2019, 24, 3268. https://doi.org/10.3390/molecules24183268

AMA Style

Zhu S, Zhou L, Gao P, Bao Y, He Y, Feng L. Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties. Molecules. 2019; 24(18):3268. https://doi.org/10.3390/molecules24183268

Chicago/Turabian Style

Zhu, Susu, Lei Zhou, Pan Gao, Yidan Bao, Yong He, and Lei Feng. 2019. "Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties" Molecules 24, no. 18: 3268. https://doi.org/10.3390/molecules24183268

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