摘要
外形是评价茶叶质量的重要指标之一,目前主要依赖人工审评的方法,客观、准确的评价外形指标对茶叶加工、销售有重要的意义。该研究提出了一种基于形状特征直方图结合支持向量机的茶叶等级识别方法。以7个等级的祁门工夫红茶为研究对象,构建图像采集系统,标定相机参数,采集各等级的茶叶图像。对图像进行灰度化、二值化处理,提取叶片的6个绝对形状特征:长度、宽度、面积、周长、最小外接矩长、宽,在此基础上计算狭长度,矩形度2个相对形状特征,生成形状特征的直方图。以直方图分布为特征向量,构建了基于BP神经网络,极限学习机(extreme learning machine,ELM),支持向量机(support vector machine,SVM),最小二乘支持向量机(least squares support vector machine,LS-SVM)的等级识别模型,并对比了不同模型的识别效果。结果表明,该文构建的图像采集系统测量精度<0.3 mm,能够准确提取形状特征参数;基于形状特征直方图的LS-SVM模型识别效果最好,识别精度为95.71%,测试集决定系数为96.2%,具有算法复杂度低,易于求解的优点。研究结果为实现茶叶的客观、数字化等级鉴定,提供了试验数据和参考方法。
Objective and accurate identification of tea grades is indispensable in tea processing and sales.Traditional grade identification often depends on human sensory judgments.This method is subjective,difficult to quantify,and has a certain error probability.The objective of this paper was to establish an objective and accurate method to identify the appearance grade of tea.In this paper,Keemun congou black tea was taken as the research object,and a SVM recognition method based on shape feature histogram multi-feature fusion was proposed.Firstly,the tea image acquisition system was built and the camera parameters were calibrated.Rectangular groups and irregular polygon groups of fix dimensions were used to test the measurement accuracy of the image acquisition system.The RGB image of tea leaves was greyed and its binary image was obtained.In order to obtain uniform shape feature parameters,the rotation of tea image was carried out with the minimum area of the leaf's outer rectangle as the constraint.Secondly,6 absolute shape features-leaf length,leaf width,leaf area,leaf perimeter,the length and width of minimum area bounding rectangle,were extracted.On this basis,2 relative shape features of length-width ratio and rectangularity were calculated.The histograms of different tea samples in different interval were further obtained,and the histogram distribution of the above characteristics was used as classifier inputs.Finally,the BP neural network,extreme learning machine(ELM),support vector machine(SVM)and least squares support vector machine(LS-SVM)were used as classifiers to classify tea samples.This result showed that the measurement accuracy of the image acquisition system constructed in this paper was less than 0.3 mm,and the shape feature parameters could be accurately extracted.When identifying all seven grades of tea samples,the recognition accuracy of BP neural network was 53.6%,the recognition accuracy of ELM was 87.86%,the recognition accuracy of SVM was 94.29%and the recognition accuracy of LS-SVM was 95.71%.The details of BP neural network classifier were as below:When 2 grades were classified,the recognition accuracy was 100%and the determination coefficient of the test set was 100.00%.When four grades were classified,the recognition accuracy was 97.5%and the determination coefficient of the test set was 93.19%.When all 7 classes were classified,the determination coefficient of test set was 53.6%.The details of ELM classifier were as below:When three grades were classified,the recognition accuracy was 90.00%When five grades were classified,the recognition accuracy was 88.00%.When SVM classifier with linear kernel function was used to identify 7 grades,the determination coefficient of test set was 86.10%.When LS-SVM classifier with linear kernel function was used to identify 7 grades,the determination coefficient of test set was 96.20%.It could be seen that the classifier based on LS-SVM had higher recognition accuracy and the best effect.There were two types of problems in the classification process:One was as the misidentification rate increased with samples amount increasing in the classification model,the second was the misidentification largely happened in adjacent classes.These problems were discussed in this paper.Through the above research,it was verified that the shape feature could be used to identify the appearance grade of Keemun congou black tea.This paper provided detailed experimental data and reference methods for the objective and digital grade identification of Keemun congou black tea.
作者
宋彦
谢汉垒
宁井铭
张正竹
Song Yan;Xie Hanlei;Ning Jingming;Zhang Zhengzhu(School of Engineering,Anhui Agricultural University,Hefei 230000,China;State Key Laboratory of Tea Plant Biology and Utilization,Anhui Agricultural University,Hefei 230000,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第23期279-286,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2017YFD0400800)
国家自然科学基金-青年基金项目(61503363)
国家重点实验室开放基金项目(安徽农业大学茶树生物学与资源利用国家重点实验室开放基金资助项目SKLTOF20170118)联合资助
关键词
图像处理
模型
神经网络
祁门红茶
外形
等级识别
支持向量机
image processing
models
neural networks
keemun black tea
appearance
identify grade
support vector machine
