轻工学报2017，Vol.32Issue(1)：103-108,6.DOI:10.3969/j.issn.2096-1553.2017.1.015
基于BP神经网络的堆积状不同品种茶叶识别研究
Varieties discrimination of accumulation teas based on BP neural network model
摘要
Abstract
A rapid and nondestructive method to discriminate the varieties of accumulation teas was proposed.The color images of the detecting teas with natural grain accumulation captured by CCD were preprocessed with filter, and analyzed by gray level co-occurrence matrix to gain texture feature of tea.Three characteristic parameters from principal component analysis were the input parameters of BP neural network model to set up pattern recognition model.The experimental results showed that the predictive accuracy was 93.8%for unknown 32 forecast samples. the system and method can meet the requirement of tea production and trade circulation.The method provided a kind of recognition technology to realize the tea varieties rapid nondestructive identification and improve the recog-nition accuracy in the process of production,processing and trade of the tea.关键词
灰度共生矩阵/BP神经网络模型/主成分分析/茶叶无损检测Key words
gray level co-occurre-nce matrix/BP neural network model/principal component analysis/tea nondestructive identifi-cation分类
信息技术与安全科学引用本文复制引用
张志峰,李世海,汤一明,乔林,吴凡,翟玉生..基于BP神经网络的堆积状不同品种茶叶识别研究[J].轻工学报,2017,32(1):103-108,6.基金项目
国家自然科学基金项目（61274012，U1304507）；河南省高等学校青年骨干教师资助计划项目（2012GGJS －118）；国家大学生创新创业训练计划项目 （）
