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

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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.

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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

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

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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

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References (25)

References

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