Study On Several Machine Learning Methods In Tea Blending Process

Tea blending was a process in tea refining aimed at referring to the national standards and standard samples of tea,blending two or more raw teas with different shapes and qualities according to a certain ratio,and making the quality standards of blended tea meet national requirements.The purpose was to improve the consistency of tea quality and stabilize tea quality.In the tea production process in China,blending experts judged whether the trial blend met the national standards through sensory evaluation,which had strong subjectivity and was difficult to quantify,which was not conducive to achieving standardization of tea quality.Although methods such as physical and chemical analysis can be used to detect tea quality,it is still difficult to achieve large-scale popularization in tea factories due to high cost,high time consumption,and inability to detect online.With the development of sensor technology and machine learning technology,computer vision and hyperspectral cameras can accurately characterize the shape and internal characteristics of tea,and machine learning methods can effectively analyze data and mine deep features.This thesis took Mee tea as the research object,based on computer vision technology and hyperspectral imaging technology,and conducted research on the identification of finished tea grades,the evaluation of the similarity between trial blends and standard samples,and the design of intelligent blending schemes in the blending process.The main results of this study were as follows:(1)Aimed at the problem of Mee tea grade discrimination,this study collected and analyzed tea leaf images through computer vision technology,and established a Mee tea grade identification model based on machine learning methods to achieve accurate classification of Mee tea grades.This thesis used digital image processing technology to analyze tea leaf images,extracted 10 external features data such as leaf length,leaf width,leaf area,leaf perimeter,and rectangularity,and constructed a histogram of tea leaf external features.Three methods,XGBoost,LMNN,and convolutional neural network,were used to establish appearance grade judgment models.The results showed that the average accuracy of the three methods in the test set with 5-fold cross-validation was higher than92%,and the method that used convolutional neural network combined with Softmax classifier had the highest classification accuracy,with an average accuracy of 97.86% in the test set.The experiment shows that machine learning methods can learn differentiated information of different categories of tea from the histogram of external features,accurately achieving the classification of eyebrow tea grades.(2)Aimed at the similarity evaluation of the trial sample and the standard sample in the process of tea blending,this thesis proposed a similarity evaluation method based on deep metric learning to evaluate the similarity between the trial blend sample and the standard sample in the tea blending process.Seven levels of standard eyebrow tea samples were used as the training set,and test set with different similarity levels was constructed by adding different proportions of semi-finished tea to the standard samples.The high spectral data of tea samples were collected,and spectral features and image features were obtained.The three types of data,including spectral data,image data,and graph fusion data,were used as inputs to the model.To construct the distance feature space,this thesis proposed a deep feature extraction network based on triplet loss and designed the Center Anchor Triplet Loss function to qualitatively judge the similarity and quantitatively measure the similarity based on the distance of tea spectral data in the feature space.The results showed that the method of graph fusion data combined with Center Anchor Triplet Loss had the highest accuracy,with a similarity judgment accuracy of 98.89% and a similarity metric accuracy of 100%.An independently evaluated model without training was used in this thesis,which achieved good results,indicating that the algorithm had good generalization ability.The research results provide a theoretical basis for the similarity evaluation of Mee tea.(3)In order to achieve the goal of intelligent tea blending,this thesis combined generative adversarial networks,deep metric learning networks,and reinforcement learning networks to construct a tea blending model that provides blending solutions for samples that meet the quality requirements of the target sample.In this experiment,4commonly used semi-finished teas for Mee tea blending were selected,and 10 tea samples with different qualities were obtained by blending them in proportion as target samples.The hyperspectral data of the aforementioned 14 tea samples were collected.Based on deep metric learning,a network was constructed to judge the similarity between the current state tea sample features and the target sample,to determine the distance of the tea target state in order to make a decision on the tea blending.To solve the problem that the spectrum cannot be directly calculated when the tea ratio changes in digital blending,a tea spectrum generator was trained based on the TF-ACGAN network,and the probability that the generated spectrum was accurately classified by the discriminator was 92.50%.In order to design the state,action,and reward functions for the Mee tea blending problem,GM-Q-learning and GM-TD3 networks were constructed based on the tea target state judgment network and the tea spectrum generator to automatically explore tea blending solutions and requested the blending ratio.The results show that the reinforcement learning method can enable the tea matching agent to accurately learn how to achieve the target state from the original state,and realize the intelligent tea matching.This thesis utilizes computer vision and hyperspectral technology to obtain tea characteristics,and builds a model based on machine learning methods to provide a digital approach for tea blending process,which involves tea grade classification,tea similarity evaluation,and blending scheme design.