https://doi.org/10.4081/jae.2024.1560
Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and convolutional neural network
Published: 16 February 2024
Abstract Views: 272
PDF: 201
HTML: 3
HTML: 3
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
A well-known agricultural product in China, Wuyuan green tea is safeguarded by national geographical indications. In addition, the processed green tea must be free of contaminants like stones and tea stalks. Nevertheless, due to their similar colors, photoelectric sorting and 2D image recognition technologies are unable to distinguish tea stalks from Wuyuan green tea. In order to address the issue of incorrect sorting brought on by similar color matching, this paper uses hyperspectral imaging technology. Using a 400–1000 nm visible and near-infrared camera, green tea with tea stalks was photographed. Furthermore, the hyperspectral image that was gathered was reduced in dimension using principal component analysis. Additionally, tea stalks were successfully identified in hyperspectral images using the convolutional neural network, which can automatically learn the corresponding features and do away with the laborious feature extraction procedure. According to the experiment’s findings, tea stalks can be recognized with an accuracy of 98.53%. The technique can meet the real production requirements and has a high recognition rate. The selection rate is as high as 97.05% following field testing.
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. (2024) “Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and convolutional neural network”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1560.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Frank Agyen Dwomoh, Xingye Zhu, Alexander Fordjour, Junping Liu, Shouqi Yuan, Hong Li, Structural design and performance characteristics of the fluidic sprinkler application technology for saving irrigation water: a review , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Emanuele Cerruto, Claudia Aglieco, Sabina Failla, Giuseppe Manetto, Parameters influencing deposit estimation when using water sensitive papers , Journal of Agricultural Engineering: Vol. 44 No. 2 (2013)
- Francesca Valenti, Simona M.C. Porto, Giovanni Cascone, Claudia Arcidiacono, Potential biogas production from agricultural by-products in Sicily. A case study of citrus pulp and olive pomace , Journal of Agricultural Engineering: Vol. 48 No. 4 (2017)
- Francesco Barreca, Giuseppe Modica, Salvatore Di Fazio, Viviana Tirella, Raimondo Tripodi, Carmelo Riccardo Fichera, Improving building energy modelling by applying advanced 3D surveying techniques on agri-food facilities , Journal of Agricultural Engineering: Vol. 48 No. 4 (2017)
- Paolo Barge, Paolo Gay, Valentina Merlino, Cristina Tortia, Passive ultra high frequency radio frequency identification systems for single-item identification in food supply chains , Journal of Agricultural Engineering: Vol. 48 No. 1 (2017)
- Meizhou Chen, Guangfei Xu, Xianghao Li, Hongda Zhao, Yongli Zhao, Peisong Diao, Yinping Zhang, Optimization design and experiment of double-helix total mixed rations preparation mixer for silage straw feed , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Maria Elena Menconi, Massimo Chiappini, Jan L.M. Hensen, David Grohmann, Thermal comfort optimisation of vernacular rural buildings: passive solutions to retrofit a typical farmhouse in central Italy , Journal of Agricultural Engineering: Vol. 48 No. 3 (2017)
- Federico Preti, Tommaso Letterio, Shallow landslide susceptibility assessment in a data-poor region of Guatemala (Comitancillo municipality) , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Marko Milan Kostić, Nataša Ljubičić, Vladimir Aćin, Milan Mirosavljević, Maša Budjen, Miloš Rajković, Nebojša Dedović, An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Mohammad Bijankhan, Vito Ferro, Dimensional analysis and stage-discharge relationship for weirs: a review , Journal of Agricultural Engineering: Vol. 48 No. 1 (2017)
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.
