基于机器视觉的工夫红茶萎凋叶水分检测

Moisture detection of black tea withered leaf based on machine vision

萎凋是工夫红茶加工的首道工序,萎凋叶水分检测主要依赖于人工经验判断,难以做到精准、客观和量化评价。本文针对萎凋叶摊放工艺,以工夫红茶萎凋在制品为研究对象,基于机器视觉技术获取萎凋叶图像的色泽和纹理特征信息,分析图像特征变量的变化规律及其与水分的关联;采用偏最小二乘法PLS(Partial least squares)、极限学习机ELM(Exterme learning machine)和支持向量机回归SVR(Support vector machine regression)方法,分别建立萎凋叶水分定量预测模型。结果表明:3种预测方法预测值和实际值的相关系数均大于0.95,非线性 SVR模型明显优于PLS模型和ELM模型,其预测集的相关系数Rp(Correlation coefficient of predication set)和预测均方根RMSEP(Root-mean-square error of prediction)分别为0.9904和0.0118,预测集绝对误差均小于0.05,相对标准偏差RPD(Relative percent deviation)值为6.6264。这说明SVR方法能够更好表征图像信息与水分之间的量化解析关系,具有更优的泛化性和鲁棒性,为萎凋加工中水分含量快速无损检测提供了解决方案,为工夫红茶水分在线检测装置的开发提供理论基础,对茶叶机械智能化的发展具有重要意义。

Withering is the first step in the processing of Congou black tea.The detection of the withered leaf moisture mainly depends on artificial experience,and it is difficult to achieve accurate,objective and quantitative evaluation.In the paper,for spreading process of withered leaf,taking the work in progress of black tea withering as the research object,based on machine vision technology,our obtained the color and texture feature information of the withering leaf images and analyzed the variation law of image feature variables and the relationship with moisture content.Using the methods of Partial least squares(PLS),Exterme learning machine(ELM) and Support vector machine regression (SVR) established a quantitative prediction model based the withering leaf moisture respectively.The results showed that the correlation coefficients between the predicted and actual values of the three prediction methods were all >0.95.The SVR model was better than the PLS model and the ELM model.The Correlation coefficient of predication set (Rp) values and the Root-mean-square error of calibration set (RMSEC) of the SVR model were 0.9904 and 0.0118,respectively.The absolute error of the prediction set were lower 0.05.The Relative percent deviation(RPD) value was 6.6264.It could be seen that Nonlinear modeling method of SVR could better characterize the quantitative analytical relationship between image information and moisture and had better generalization and robustness.The study provided a solution for the rapid non-destructive detecting of moisture content during the withering process,and a theoretical basis for Congfu black tea detection device of moisture online,which also produced a great significance to the development of tea machinery intelligence.