Online Evaluation System of Tea Quality Based on Computer Vision

JIN Shanfeng; WANG Dongxin; HUANG Junshi; XIONG Aihua; AI Shirong; LIU Peng; WU Jingpeng; WU Ruimei

doi:10.13386/j.issn1002-0306.2020100152

Volume 42 Issue 14

Jul. 2021

Turn off MathJax

Article Contents

Abstract

References

Science and Technology of Food Industry > 2021 > 42(14): 219-225. > DOI: 10.13386/j.issn1002-0306.2020100152

JIN Shanfeng, WANG Dongxin, HUANG Junshi, et al. Online Evaluation System of Tea Quality Based on Computer Vision [J]. Science and Technology of Food Industry, 2021, 42(14): 219−225. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020100152.

Citation:

PDF (1038 KB)

Online Evaluation System of Tea Quality Based on Computer Vision

1.
College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
2.
Wuyuan County Zhang Gongshan Tea Industry Limited Company, Shangrao 333200, China
3.
College of Software, Jiangxi Agricultural University, Nanchang 330045, China
4.
Jiangxi Vocational College of Mechanical & Electrical Technology, Nanchang 330013, China

More Information

Received Date: October 20, 2020
Available Online: May 21, 2021

Graphical Abstract

Abstract

Abstract

In order to realize the online evaluation and automatic grading of tea quality, and eliminate the defects of artificial sensory evaluation of tea quality, this paper focused on development ofa set of tea quality online evaluation and automatic grading system basing on computer vision technology. The software was designed by Open CV and Visual C++ realizingthe online evaluation for tea quality, combining supervised orthogonal locality preserving projections (SOLPP) to reduce the dimensionality for image features variables. The sensory evaluation models of tea quality were respectively developed based on RF (random forest), BP-ANN (back-propagation artificial neural network) and SVR (relevance vector machine), by contrast, the performance of based RF model was the optimal. The system automatically completed the image acquisition of tea samples, original image preprocessing, feature extraction, and the grade evaluation to the tea samples based on the developed models. According to the evaluation results, the tea samples were transfered to the corresponding grading tank by the grading and collecting device droved by the control system. The prototype testing results showed that the classification accuracy rate of the Wuyuan Xianzhi green tea and Biluochun green tea reached more than 93%. This developed system had simple structure with stable operation, and the samples could be accurately sent to the corresponding grade tanks, which could meet the requirements of online evaluation of tea grade.
- computer vision,
- tea quality,
- supervised orthogonal locality preserving projections (SOLPP),
- random forest,
- online evalutation

FullText(HTML)

References (25)

References

[1]	欧伊伶, 张娅楠, 覃丽, 等. 茶叶色香味品质评价方法研究进展[J]. 食品工业科技,2019,40(6):342−347, 360.
[2]	温立香, 张芬, 何梅珍, 等. 茶叶品质评价技术的研究现状[J]. 食品研究与开发,2018,39(15):197−204. doi: 10.3969/j.issn.1005-6521.2018.15.038
[3]	Liu C, Lu W, Gao B, et al. Rapid identification of chrysanthemum teas by computer vision and deep learning[J]. Food Science & Nutrition,2020,8(4):1968−1977.
[4]	Bakhshipour A, Zareiforoush H, Bagheri I. Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features[J]. Journal of Food Measurement and Characterization,2020,14(9):1−15.
[5]	董春旺, 朱宏凯, 周小芬, 等. 基于机器视觉和工艺参数的针芽形绿茶外形品质评价[J]. 农业机械学报,2017,48(9):38−45. doi: 10.6041/j.issn.1000-1298.2017.09.005
[6]	Wang S H, Phillips P, Liu A J, et al. Tea category identification using computer vision andgeneralized eigenvalue proximal SVM[J]. Fundamenta Informaticae,2017,151(1-4):325−339. doi: 10.3233/FI-2017-1495
[7]	Laddi A, Shashi S, Kumar A, et al. Classification of tea grains based upon image texture feature analysis under different illumination conditions[J]. Journal of Food Engineering,2013,115(2):226−231. doi: 10.1016/j.jfoodeng.2012.10.018
[8]	董春旺, 梁高震, 安霆, 等. 红茶感官品质及成分近红外光谱快速检测模型建立[J]. 农业工程学报,2018,34(24):306−313. doi: 10.11975/j.issn.1002-6819.2018.24.037
[9]	Ouyang Q, Liu Y, Chen Q S, et al. Intelligent evaluation ofcolor sensory quality of black tea by visible-near infraredspectroscopy technology: A comparison of spectra and colordata information[J]. Spectrochimica Acta Part A: Molecularand Biomolecular Spectroscopy,2017,180:91−96. doi: 10.1016/j.saa.2017.03.009
[10]	Calixto R R, Neto L G P, Cavalcante T D S, et al. A computer vision model development for size and weight estimation of yellow melon in the Brazilian northeast[J]. Scientia Horticulturae,2019,256:108521. doi: 10.1016/j.scienta.2019.05.048
[11]	Arthur Z da Costa, Hugo E H Figueroa, Juliana A Fracarolli. Computer vision based detection of external defects on tomatoes using deep learning[J]. Biosystems Engineering,2020,190:131−144. doi: 10.1016/j.biosystemseng.2019.12.003
[12]	杨祖彬, 曾莉红. 脐橙分选包装表面损伤识别算法设计研究[J]. 食品工业科技,2014,35(1):264−269.
[13]	魏文松, 邢瑶瑶, 李永玉, 等. 适于餐厅与家庭的叶菜外部品质在线检测与分级系统[J]. 农业工程学报,2018,34(5):264−273. doi: 10.11975/j.issn.1002-6819.2018.05.035
[14]	Baneh N M, Navid H, Kafashan J. Mechatronic components in apple sorting machines with computer vision[J]. Journal of Food Measurement and Characterization,2018,12(2):1135−1155. doi: 10.1007/s11694-018-9728-1
[15]	胡潇, 熊爱华, 黄俊士, 等. 计算机视觉结合引导滤波方法快速量化茶叶叶底品质[J]. 江西农业大学报,2019,41(3):601−609.
[16]	余洪, 吴瑞梅, 艾施荣, 等. 基于PCA-PSO-LSSVM的茶叶品质计算机视觉分级研究[J]. 激光杂志,2017,38(1):51−54.
[17]	刘鹏, 吴瑞梅, 杨普香, 等. 基于计算机视觉技术的茶叶品质随机森林感官评价方研究[J]. 光谱学与光谱分析,2019,39(1):193−198.
[18]	赵改名, 焦阳阳, 祝超智, 等. 基于极差分析法与主成分分析法研究新型牛肉薄饼加工工艺[J]. 现代食品科技,2019,35(11):144−151.
[19]	Zhang Y H, Deng X Y, Zhou X, et al. Yachine with kernel principal component analysis based on acoustic signals[J]. International Journal of Watermelon Ripeness Detection via Extreme Learning Mttern Recognition and Artificial Intelligence,2019,33(8):17.
[20]	Song B , Baik D K. A facial expression recognition algorithm with supervised orthogonal locality preserving projection[J]. Journal of Computational and Theoretical Nanoscience,2016,13(11):8495−8504. doi: 10.1166/jctn.2016.6003
[21]	郭金玉, 仲璐璐, 李元. 基于统计差分LPP的多模态间歇过程故障检测[J]. 计算机应用研究,2019,36(1):123−126.
[22]	张善文, 张传雷, 程雷. 基于监督正交局部保持映射的植物叶片图像分类方法[J]. 农业工程学报,2013,29(5):125−131.
[23]	卢维学, 吴和成, 万里洋. 基于融合随机森林算法的PLS对降水量的预测[J]. 统计与决策,2020,36(18):27−31.
[24]	刘鹏, 艾施荣, 杨普香, 等. 非线性流形降维方法结合近红外光谱技术快速鉴别不同海拔的茶叶[J]. 茶叶科学,2019,39(6):715−722. doi: 10.3969/j.issn.1000-369X.2019.06.011
[25]	Liu P, Wen Y, Huang J, et al. A novel strategy of near-infrared spectroscopy dimensionality reduction for discrimination of grades, varieties and origins of green tea[J]. Vibrational Spectroscopy,2019,105:102984. doi: 10.1016/j.vibspec.2019.102984

[1]	SUN Renjie, HE Qin, WU Dezhi. Visual Analysis of Dendrobium candidum Related Research Based on CiteSpace Knowledge Map and Patent Bibliometrics[J]. Science and Technology of Food Industry, 2023, 44(20): 322-330. DOI: 10.13386/j.issn1002-0306.2022120168
[2]	SU Qingyu, CHANG Xiaomin, LIU Yaran, XU Xiaoqing, LI Jiaze, ZHU Yuxuan, SONG Hao, ZHU Baoqing. Current Status Research of Projective Mapping in Food Research and Development[J]. Science and Technology of Food Industry, 2022, 43(16): 390-399. DOI: 10.13386/j.issn1002-0306.2021070202
[3]	YANG Xin, QIN Lina, JIANG Xianzhang. Analysis of Codon Usage Bias in the Genome of Trichoderma reesei[J]. Science and Technology of Food Industry, 2022, 43(6): 141-149. DOI: 10.13386/j.issn1002-0306.2021110338
[4]	WANG Zu-lian, CHEN Qing, GAO Jia, LUO Fang-yao, TANG Yue-ming, TIAN Yu-xiao, GUO Yun-jian. Effect of Packaging Film Permeability on the Quality of MAP Storage of Hotbed Chives during Cold Storage[J]. Science and Technology of Food Industry, 2021, 42(1): 304-311. DOI: 10.13386/j.issn1002-0306.2020030031
[5]	ZHOU Ting, HE Dan, HUANG Hui, HAO Shu-xian, LI Lai-hao, WEI Ya, CEN Jian-wei. Effect and analysis of MAP on volatile flavor compounds in sturgeon(Huso dauricused × sturger schrenckii) caviar[J]. Science and Technology of Food Industry, 2016, (15): 260-264. DOI: 10.13386/j.issn1002-0306.2016.15.042
[6]	WANG Dong, WANG Zhe- di, MA Yue, LI Qi. Impact of serving conditions on consumer preferences for Chinese rice wine[J]. Science and Technology of Food Industry, 2016, (03): 127-130. DOI: 10.13386/j.issn1002-0306.2016.03.018
[7]	JIANG Li-hong, ZHOU Ying-zhe, HONG Qing, LIU Xiao-ming, TIAN Feng-wei, ZHAO Jian-xin, ZHANG Hao, CHEN Wei. Flavor characteristics and consumer preference for the commercial aged Cheddar cheese[J]. Science and Technology of Food Industry, 2014, (23): 275-281. DOI: 10.13386/j.issn1002-0306.2014.23.050
[8]	CHANG Xiang-yang, HU Hao. Research of consumer behavior for food safety attributes in the view of choice experiment[J]. Science and Technology of Food Industry, 2014, (11): 273-277. DOI: 10.13386/j.issn1002-0306.2014.11.051
[9]	Research on preservation effect influence of pre-preparing method on fresh beef products packaged in MAP[J]. Science and Technology of Food Industry, 2013, (02): 310-314. DOI: 10.13386/j.issn1002-0306.2013.02.080
[10]	Research on quadric orthogonal regression experiment on MAP craft of waxberry[J]. Science and Technology of Food Industry, 2012, (19): 326-328. DOI: 10.13386/j.issn1002-0306.2012.19.066

Supplements (0)

Cited By

Cited by

Periodical cited type(6)

1.	杨蕾，冯洪涛，何雪峰，杨乾栩，蒋梦菲，张涛. 基于MFA的消费者与专家卷烟抽吸感知及风味感知分析. 中国市场. 2024(28): 99-102 .
2.	常晓敏，赵慧敏，张悦，刘佳妮，李玄烨，李晓龙，初柏君，王翔宇，朱保庆. 桌布法结合极化投影地图法剖析葵花籽油香味特征. 食品安全质量检测学报. 2023(13): 184-192 .
3.	侯姣靓. XLSTAT在感官评价数据分析中的应用. 上海轻工业. 2023(03): 130-133 .
4.	单冰淇，刘松昱，王春光，游睿晗，宋昊，钟葵，史波林，朱保庆. 开放式提问调查在食品研究与开发中的研究现状. 食品工业科技. 2022(12): 468-474 . 本站查看
5.	陈亦新，兰义宾，问亚琴，刘雅冉，陈奎汛，宋昊，朱保庆. 自选特性排序剖面法在食品研究与开发中的应用及研究现状. 食品工业科技. 2022(13): 475-483 . 本站查看
6.	苏庆宇，常晓敏，刘雅冉，许晓青，李佳泽，朱雨萱，宋昊，朱保庆. 投影地图法在食品研究与开发中的研究现状. 食品工业科技. 2022(16): 390-399 . 本站查看