基于图像特征与智能算法的龙井茶等级鉴定与外形审评方法研究

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中图分类号：S571.1；S126 文献标识码：A 文章编号：1000-369X（2025）06-1083-12

Abstract： Longjing teaappearance features are crucialforassessing tea quality.In order to intelligently evaluate the appearance characteristics ofLongjing tea and quickly recognize the grades，a method combining image features and intellgent algorithms for appearance quality assssment was proposed. Images ofthe six grades of the standard Dafo

Longjing tea werecolected and their shape，color，and texture features were quantified and extracted to constructa database of tea appearance grade characteristics. The appearance features were fused and input into five machine learning models，namely，Decision Tree，Random Forest，，K-Nearest Neighbor， Gausian Bayesian，and Support Vector Machine，for grade recognition training. Three convolutional neural networks， VGG16，ResNet18 and DenseNet121 were compared to build a deep learning model for Longjing tea appearance evaluation.Then the model was optimized by optimizers and learning rate decay. The results show that the fusion of shape and texture data combined with Support Vector Machine was the optimal model for grade recognition，with an accuracy of 91.14% and an F1 score of 91.20% . The ResNet18 network structure was chosen to establish the optimal model for appearance evaluation. After optimization by Adadelta optimizer and CosineAnnealingLR learning rate decay， the recognition accuracies ofLongjing tea's flatness，straightness，tenderness，and color all improved，reaching 99.21% （204号 99.51% 99.56% ，and 99.68% ，respectively. This study provided a theoretical foundation for the digital evaluation of the appearance quality of tea.

Keywords： Longjing tea， image recognition， grade，appearance，machine learning

龙井茶品质评价作为茶叶质量管控的核心环节，长期依赖破坏性理化检测与人工感官审评。（剩余15574字）