基于机器视觉的茶叶微波杀青中品质变化与预测研究

Study on the Tea Quality Changes and Predictions during the Microwave Fixation Process by Machine Vision

茶多酚、氨基酸、含水率是茶叶品质的重要指标,传统检测方法周期长且过程复杂。本研究利用机器视觉对微波杀青过程中茶叶的色泽和纹理特征实时监测,在线检测含水率,同时检测茶多酚和氨基酸含量。结果表明,色泽、纹理特征与含水率、茶多酚、氨基酸含量均呈规律性变化且显著相关。对色泽和纹理特征进行主成分分析,以前3个主成分为输入建立极限学习机(ELM)、遗传神经网络(GA-BP)、卷积神经网络(CNN)模型对品质成分含量进行预测。结果表明,ELM、GA-BP、CNN模型分别适用于含水率、茶多酚含量和氨基酸含量的预测,精度均在0.99以上。研究表明,通过实时监测茶叶的色泽和纹理特征来预测其在杀青过程中含水率、茶多酚和氨基酸含量是可行的。

Tea polyphenol,amino acid and moisture contents are important indicators of tea quality.Traditional detection methods have long cycles and complex processes.In this paper,machine vision was used to monitor the color and texture of tea leaves in real time during the microwave fixation process.The moisture content was detected online and the tea polyphenol and amino acid contents were also measured.The results show that the color,texture features and moisture content,tea polyphenol,amino acid contents all showed regular changes during the fixation process and had significant correlations.The principal component analysis was used to analyze the color and texture features and the first 3 principal components were taken to establish extreme learning machine(ELM),genetic neural network(GA-BP),and convolutional neural network(CNN)models to predict the quality.The results show that ELM,GA-BP and CNN models were more suitable for the prediction of moisture,tea polyphenol and amino acid contents,respectively,and their accuracies were all above 0.99.The research results show that it is feasible to predict the moisture,tea polyphenol and amino acid contents during the fixation process by monitoring the color and texture features of tea in real time.