Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Na Wu; Yu Zhang; Risu Na; Chunxiao Mi; Susu Zhu; Yong He; Chu Zhang

doi:10.1039/c8ra10335f

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

RSC Adv. 2019 Apr 25;9(22):12635-12644. doi: 10.1039/c8ra10335f. eCollection 2019 Apr 17.

Authors

Na Wu^{1

2}, Yu Zhang³, Risu Na⁴, Chunxiao Mi^{1

2}, Susu Zhu^{1

2}, Yong He^{1

2}, Chu Zhang^{1

2}

Affiliations

¹ College of Biosystems Engineering and Food Science, Zhejiang University 866 Yuhangtang Road Hangzhou 310058 China yhe@zju.edu.cn.
² State Key Laboratory of Modern Optical Instrumentation, Zhejiang University Hangzhou 310058 China.
³ Zhejiang Technical Institute of Economics Hangzhou 310018 China.
⁴ Chifeng Academy of Agricultural and Animal Sciences Chifeng 024031 China.

Abstract

Variety identification of seeds is critical for assessing variety purity and ensuring crop yield. In this paper, a novel method based on hyperspectral imaging (HSI) and deep convolutional neural network (DCNN) was proposed to discriminate the varieties of oat seeds. The representation ability of DCNN was also investigated. The hyperspectral images with a spectral range of 874-1734 nm were primarily processed by principal component analysis (PCA) for exploratory visual distinguishing. Then a DCNN trained in an end-to-end manner was developed. The deep spectral features automatically learnt by DCNN were extracted and combined with traditional classifiers (logistic regression (LR), support vector machine with RBF kernel (RBF_SVM) and linear kernel (LINEAR_SVM)) to construct discriminant models. Contrast models were built based on the traditional classifiers using full wavelengths and optimal wavelengths selected by the second derivative (2nd derivative) method. The comparison results showed that all DCNN-based models outperformed the contrast models. DCNN trained in an end-to-end manner achieved the highest accuracy of 99.19% on the testing set, which was finally employed to visualize the variety classification. The results demonstrated that the deep spectral features with outstanding representation ability enabled HSI together with DCNN to be a reliable tool for rapid and accurate variety identification, which would help to develop an on-line system for quality detection of oat seeds as well as other grain seeds.