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
- Giovannni Molari, Michele Mattetti, Daniela Perozzi, Eugenio Sereni, Monitoring of the tractor working parameters from the CAN-Bus , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Damiano Coppolecchia, Davide Gardoni, Cecilia Baldini, Federica Borgonovo, Marcella Guarino, The influence on biogas production of three slurry-handling systems in dairy farms , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Alvaro Marucci, Adolfo Gusman, Barbara Pagniello, Andrea Cappuccini, Limits and prospects of photovoltaic covers in Mediterranean greenhouses , Journal of Agricultural Engineering: Vol. 44 No. 1 (2013)
- Ferruccio Giametta, Flavio Fucci, Pasquale Catalano, Giovanna La Fianza, Thermo-fluid-dynamic modelling of a cold store for cheese maturation , Journal of Agricultural Engineering: Vol. 43 No. 4 (2012)
- Qing Guo, Huihuang Xia, A review of the discrete element method/modelling in agricultural engineering , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Wenjun Wang, Sihao Zhang, Jingyu Li, Pengfei Zhang, Yulong Chen, Effects of the twin-row planter with subsoiling on soybean growth and yield in northern China , Journal of Agricultural Engineering: Vol. 53 No. 3 (2022)
- Ayodeji Nathaniel Oyedeji, Umar Ali Umar, Laminu Shettima Kuburi, Albright Abu Edet, Yau Mukhtar, Development and performance evaluation of an oil palm harvesting robot for the elimination of ergonomic risks associated with oil palm harvesting , Journal of Agricultural Engineering: Vol. 53 No. 3 (2022)
- Alessandro Parenti, Paolo Spugnoli, Piernicola Masella, Lorenzo Guerrini, Stefano Benedettelli, Stefano Di Blasi, Comparison of grape harvesting and sorting methods on factors affecting the must quality , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Francesco Bettella, Vincenzo D'Agostino, Lucia Bortolini, Drainage flux simulation of green roofs under wet conditions , Journal of Agricultural Engineering: Vol. 49 No. 4 (2018)
- Yiming Xiao, Jianhua Wang, Hongyi Xiong, Fangjun Xiao, Renhuan Huang, Licong Hong, Bofei Wu, Jinfeng Zhou, Yongbin Long, Yubin Lan, Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
<< < 19 20 21 22 23 24 25 26 27 28 > >>
You may also start an advanced similarity search for this article.
