Format

Send to

Choose Destination
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Mar 6;234:118237. doi: 10.1016/j.saa.2020.118237. [Epub ahead of print]

Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion.

Author information

1
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road Hefei, China. Electronic address: weng_1989@126.com.
2
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road Hefei, China.
3
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road Hefei, China. Electronic address: graymagpie@163.com.

Abstract

The phenomena of rice adulteration and shoddy rice arise continuously in high-quality rice and reduce the interests of producers, consumers and traders. Hyperspectral imaging (HSI) was conducted to determine rice variety using a deep learning network with multiple features, namely, spectroscopy, texture and morphology. HSI images of 10 representative high-quality rice varieties in China were measured. Spectroscopy and morphology were extracted from HSI images and binary images in region of interest, respectively. And texture was obtained from the monochromatic images of characteristic wavelengths which were highly correlated with rice varieties. A deep learning network, namely principal component analysis network (PCANet), was adopted with these features to develop classification models for determining rice variety, and machine learning methods as K-nearest neighbour and random forest were used to compare with PCANet. Meanwhile, multivariate scatter correction, standard normal variate, Savitzky-Golay smoothing and Savitzky-Golay's first-order were applied to eliminate spectral interference, and principal component analysis (PCA) was performed to obtain the main information of high-dimensional features. Multi-feature fusion improved recognition accuracy, and PCANet demonstrated considerable advantage in classification performance. The best result was achieved by PCANet with PCA-processed spectroscopic and texture features with correct classification rates of 98.66% and 98.57% for the training and prediction sets, respectively. In summary, the proposed method provides an accurate identification of rice variety and can be easily extended to the classification, attribution and grading of other agricultural products.

KEYWORDS:

Deep learning network; High-quality rice; Hyperspectral imaging; Multi-feature fusion

PMID:
32200232
DOI:
10.1016/j.saa.2020.118237

Conflict of interest statement

Declaration of competing interest The authors declare no conflict of interest.

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center