https://doi.org/10.4081/jae.2024.1560

Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and Convolutional Neural Network

Early Access
Published: 16 February 2024
Convolutional neural network, green tea, hyperspectral image, principal component analysis, tea stalks
Abstract Views: 109
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Authors

Xin Yu
College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, China.
Ling Zhao
zhaoling@lcu.edu.cn
College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, China.
Zongbin Liu
College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, China.
Yiqing Zhang
College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, China.

Wuyuan green tea is a famous agricultural product in China and a product protected by national geo-graphical indications. The processed green tea also needs to remove impurities, such as stones, tea stalks, etc. However, tea stalks cannot be classified from Wuyuan green tea using photoelectric sorting and 2D image recognition technology since they have similar colors. This paper adopts hyperspectral imaging technology to solve the problem of inaccurate sorting caused by their similar colors. Green tea containing tea stalks was imaged using a visible and near-infrared camera with a wavelength of 400nm-1000nm. What’s more, Principal Component Analysis (PCA) was adopted to reduce the dimension of the col-lected hyperspectral image. And the Convolutional Neural Network (CNN) was used constructively to identify tea stalks in hyperspectral image, the CNN can automatically learn the corresponding features, avoid the complex feature extraction process. The experimental results showed that the recognition accuracy for tea stalks reaches 98.53%. The method has a high recognition rate and can meet the actual production requirements. After field testing, the selection rate is as high as 97.05%.

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Citations

Crossref
Scopus
Google Scholar
Europe PMC
Chen P., Wu T. Detection of impurities in tea using Hough transform after removing shadows[J]. Mechanical Engineering and Automation, 2014(05): 63-65.
Chen S., Zhang C. A fast image sorting method of tea stalk color sorter[J]. Journal of Hefei Univer-sity(Natural Science Edition), 2013, 23(04): 36-41.
Celik T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering[J]. IEEE geoscience and remote sensing letters, 2009, 6(4): 772-776. DOI: https://doi.org/10.1109/LGRS.2009.2025059
ElMasry G, Sun D W. Principles of hyperspectral imaging technology[M]. Hyperspectral imaging for food quality analysis and control. Academic Press, 2010: 3-43. DOI: https://doi.org/10.1016/B978-0-12-374753-2.10001-2
Fu L, Okamoto H, Shibata Y, et al. Distinguishing overripe berries of Japanese blue honeysuckle using hyperspectral imaging analysis[J]. Engineering in agriculture, environment and food, 2014, 7(1): 22-27. DOI: https://doi.org/10.1016/j.eaef.2013.12.004
Hu L. Wuyuan green tea: green bushes are all over the mountains and fields, and every household has fragrant tea[J]. Jiangxi Agriculture, 2015(02): 24-25.
Hu B., He X. Key technologies for primary processing of Wuyuan green tea[J]. Agricultural Devel-opment and Equipment, 2016(05): 138.
Jimenez L O, Landgrebe D A. Hyperspectral data analysis and supervised feature reduction via pro-jection pursuit[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(6): 2653-2667. DOI: https://doi.org/10.1109/36.803413
LIU Y. Status and development of hyperspectral imaging remote sensing payload technology[J]. Acta Remote Sensing, 2021, 25(01): 439-459. DOI: https://doi.org/10.11834/jrs.20210283
Lu R. Detection of bruises on apples using near-infrared hyperspectral imaging[J]. Transactions of the ASAE, 2003, 46(2): 523. DOI: https://doi.org/10.13031/2013.12941
Lu B, Dao P D, Liu J, et al. Recent advances of hyperspectral imaging technology and applications in agriculture[J]. Remote Sensing, 2020, 12(16): 2659. DOI: https://doi.org/10.3390/rs12162659
Panda A, Pachori R B, Sinnappah-Kang N D. Classification of chronic myeloid leukemia neutrophils by hyperspectral imaging using Euclidean and Mahalanobis distances[J]. Biomedical Signal Pro-cessing and Control, 2021, 70: 103025. DOI: https://doi.org/10.1016/j.bspc.2021.103025
Quan Q. Research and development of tea stem picker in China [J]. China Tea, 2018, 40 (07): 6-11.
Ren Z. A new intermittent tea stem picker [J]. China Tea, 1989 (02): 21. DOI: https://doi.org/10.5979/cha.1989.21
Ren J, Zabalza J, Marshall S, et al. Effective feature extraction and data reduction in remote sensing using hyperspectral imaging [applications corner][J]. IEEE Signal Processing Magazine, 2014, 31(4): 149-154. DOI: https://doi.org/10.1109/MSP.2014.2312071
Sun J., Xiong S., Wang L. Single chip control of tea stem picking machine[J]. Mechanical and Electrical Engineering, 1997(03): 23-24.
Shaheen F, Verma B, Asafuddoula M. Impact of automatic feature extraction in deep learning archi-tecture[C]. 2016 International conference on digital image computing: techniques and applications (DICTA). IEEE, 2016: 1-8. DOI: https://doi.org/10.1109/DICTA.2016.7797053
Uğuz H. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm[J]. Knowledge-Based Systems, 2011, 24(7): 1024-1032. DOI: https://doi.org/10.1016/j.knosys.2011.04.014
Windham W R, Lawrence K C, Park B, et al. Analysis of reflectance spectra from hyperspectral images of poultry carcasses for fecal and ingesta detection[C]. Imaging Spectrometry Ⅷ. SPIE, 2002, 4816: 317-324. DOI: https://doi.org/10.1117/12.451653
Xie P., Tao D., Ren S. Development status and trend of tea stem picker [J]. Research on Agricultural Mechanization, 2004 (01): 44-45.

How to Cite

Yu, X., Zhao, L., Liu, Z. and Zhang, Y. (2024) “Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and Convolutional Neural Network”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1560.
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