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

摘要/Abstract

摘要： 龙井茶外形特征对其茶叶品质判断具有重要意义。为智能评价龙井茶外形并快速识别等级,提出了图像特征与智能算法相结合的外形品质评价方法。采集6个等级大佛龙井茶标准样图像,量化提取形状、色泽、纹理特征,构建茶叶外形等级特征数据库。将外形特征以数据级融合输入决策树、随机森林、K近邻算法、高斯朴素贝叶斯和支持向量机5种机器学习模型,进行等级识别训练。对比VGG16、ResNet18和DenseNet121 3种卷积神经网络建立外形评价深度学习模型,通过优化器和学习率衰减策略优化模型。结果显示,形状+纹理的融合数据结合支持向量机为等级识别最优模型,准确率为91.14%,F₁值为91.20%。选用ResNet18网络结构建立外形审评模型最佳,经Adadelta优化器与CosineAnnealingLR学习率衰减策略优化后,龙井茶外形的扁平度、挺直度、嫩度和色泽识别准确率均有提高,分别达到99.21%、99.51%、99.56%、99.68%。研究结果为茶叶外形品质的数字化评价提供了一定的理论基础。

关键词: 龙井茶, 图像识别, 等级, 外形, 机器学习

Abstract: Longjing tea appearance features are crucial for assessing tea quality. In order to intelligently evaluate the appearance characteristics of Longjing tea and quickly recognize the grades, a method combining image features and intelligent algorithms for appearance quality assessment was proposed. Images of the six grades of the standard Dafo Longjing tea were collected and their shape, color, and texture features were quantified and extracted to construct a 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, Gaussian 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 of Longjing tea’s flatness, straightness, tenderness, and color all improved, reaching 99.21%, 99.51%, 99.56%, and 99.68%, respectively. This study provided a theoretical foundation for the digital evaluation of the appearance quality of tea.

Key words: Longjing tea, image recognition, grade, appearance, machine learning

中图分类号:

S571.1
S126

黄旦益, 赵珠蒙, 范冬梅, 林杰, 陆娅婷, 王校常, 李腊梅. 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[J]. 茶叶科学, 2025, 45(6): 1083-1094.

HUANG Danyi, ZHAO Zhumeng, FAN Dongmei, LIN Jie, LU Yating, WANG Xiaochang, LI Lamei. Study on Longjing Tea Grade Identification and Appearance Evaluation Method Based on Image Features and Intelligent Algorithm[J]. Journal of Tea Science, 2025, 45(6): 1083-1094.

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