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Design of a Moisture Content Detection System for Yinghong No. 9 Tea Leaves Based on Machine Vision

文献类型： 外文期刊

作者： Wang, Feiren 1 ; Xie, Boming 2 ; Lue, Enli 2 ; Zeng, Zhixiong 2 ; Mei, Shuang 2 ; Ma, Chengying 4 ; Guo, Jiaming 2 ;

作者机构： 1.Guangdong Mech & Elect Coll Technol, Automot Inst, Guangzhou 510550, Peoples R China

2.South China Agr Univ, Coll Engn, Guangzhou 510640, Peoples R China

3.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

4.Guangdong Acad Agr Sci, Tea Res Inst, Guangdong Prov Key Lab Tea Plant Resources Innovat, Guangzhou 510640, Peoples R China

关键词： tea leaves moisture content; machine vision; back propagation neural network; particle swarm optimization; genetic algorithm

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.7； 五年影响因子：2.9 ）

ISSN：

年卷期： 2023 年 13 卷 3 期

页码：

收录情况： SCI

摘要： The moisture content of Yinghong No. 9 tea leaves is an important indicator for their processing. The traditional method used to detect the moisture content of tea leaves is not suitable for large-scale production. To improve the efficiency of tea processing, a moisture content detection system for Yinghong No. 9 tea leaves based on machine vision was developed, and the relationship between the moisture content and the fresh tea leaves was researched. Firstly, nine color features and five texture features of the tea leaves images were extracted, and two different tea leaves databases were constructed based on linear discriminant analysis (LDA) and principal component analysis (PCA). Secondly, two models of moisture prediction for fresh tea leaves were built using a backpropagation (BP) neural network, which were then optimized by particle swarm optimization (PSO) and a genetic algorithm (GA), respectively. After, the two preprocessing methods and the two optimization algorithms were cross-combined to optimize the models for moisture content prediction. Finally, the models above were filtered using segmental analysis for the segmental moisture content prediction. It was verified by experiments that the coefficient of determination (R-2) of the combined model of PCA-GA-BP and PCA-PSO-BP was 94.1073%, the RMSE was 1.1490%, and the MAE was 0.9982%. The results of this paper can help in the instantaneous detection of the moisture content of fresh tea leaves during processing, improving the production efficiency of Yinghong No. 9 tea.

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