数字农科院2.0

Data fusion strategy for rapid prediction of moisture content during drying of black tea based on micro-NIR spectroscopy and machine vision

文献类型： 外文期刊

作者： Sheng, Xufeng;Zan, Jiezhong;Jiang, Yongwen;Shen, Shuai;Li, Li;Yuan, Haibo

作者机构：

关键词： Congou black tea;Micro-NIR spectroscopy;Machine Vision;Moisture content;Drying in-process products;Data Fusion

期刊名称： OPTIK

ISSN： 0030-4026

年卷期： 2023 年 276 卷

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Drying is an important process in black tea processing, meanwhile, the moisture content is an important factor in determining the drying quality. However, at present, there is a lack of effective real-time detection methods for the moisture content of black tea during the drying. Therefore, we explored the analysis of spectral data and image color and texture feature data based on micro-near-infrared spectroscopy(micro-NIRS) and machine vision data fusion tech-nology, and analyzed the spectral data and image color and texture feature data based on micro-NIR spectroscopy and machine vision data fusion technology, a quantitative prediction model for the drying moisture content of black tea was developed by LS-SVM. The results showed that the prediction accuracy of the prediction model established based on the middle-level data fusion achieved the best results and the correlation coefficient (Rp) and root mean square error of prediction (RMSEP) of the prediction set were 0. 9696 and 0.0016, respectively, and the relative deviation (RPD) was 4.0846. This study shows that data fusion based on the fusion of spectral and image technologies has a strong predictive capability for the drying process of black tea, and has some guiding significance for controlling the drying quality of black tea.

分类号：

  • 相关文献

[1]Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. Zhongyuan Liu,Rentian Zhang,Chongshan Yang,Bin Hu,Xin Luo,Yang Li,Chunwang Dong. 2022

[2]Identification Of Similar Chinese Congou Black Teas Using An Electronic Tongue Combined With Pattern Recognition. Bian, Zhuang,Huang, Danyi,Wang, Yinmao,Fan, Dongmei,Wang, Xiaochang,Qiu, Qinli. 2019

[3]Rutin, Gamma-Aminobutyric Acid, Gallic Acid, And Caffeine Negatively Affect The Sweet-Mellow Taste Of Congou Black Tea Infusions. Li, J, Yao, YF, Wang, JQ, Hua, JJ, Wang, JJ, Yang, YQ, Dong, CW, Zhou, QH, Jiang, YW, Deng, YL, Yuan, HB. 2019

[4]Identification of aroma-active compounds responsible for the floral and sweet odors of Congou black teas using gas chromatography-mass spectrometry/olfactometry, odor activity value, and chemometrics. Xue, Jinjin,Guo, Guiyi,Liu, Panpan,Chen, Lin,Wang, Weiwei,Zhang, Jianyong,Yin, Junfeng,Ni, Dejiang,Engelhardt, Ulrich H.,Jiang, Heyuan. 2022

[5]Exploration of the effects of geographical regions on the volatile and non-volatile metabolites of black tea utilizing multiple intelligent sensory technologies and untargeted metabolomics analysis. Lilei Wang,Jialing Xie,Yiwen Miao,Qiwei Wang,Jiajing Hu,Yongwen Jiang,Jinjin Wang,Huarong Tong,Haibo Yuan,Yanqin Yang. 2024

[6]Evaluation of the quality grade of Congou black tea by the fusion of GC-E-Nose, E-tongue, and E-eye. Lilei Wang,Jialing Xie,Qiwei Wang,Jiajing Hu,Yongwen Jiang,Jinjin Wang,Huarong Tong,Haibo Yuan,Yanqin Yang. 2024

[7]Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. Qiang, Lu,Lie, Deng,Cai Jianrong,Bin, Liu,Zhang Yajing. 2014

[8]Plant Identification Based on Artificial Intelligence. Hou Chun Sheng,Zhou Ting. 2011

[9]Bionic technology and deep learning in agricultural engineering: Current status and future prospects. Chunlei Tu,Jinxia Li,Xingsong Wang,Shen Cheng,Jie Li. 2021

[10]Intelligent Monitoring System of Migratory Pests Based on Searchlight Trap and Machine Vision. Sun, Guojia;Liu, Shuhua;Luo, Haolun;Feng, Zelin;Yang, Baojun;Luo, Ju;Tang, Jian,Yao, Qing,Xu, Jiajun. 2022

[11]Application status and challenges of machine vision in plant factory—A review. Zhiwei Tian,Wei Ma,Qichang Yang,Famin Duan. 2022

[12]A Review of Posture Detection Methods for Pigs Using Deep Learning. Chen, Zhe,Lu, Jisheng,Wang, Haiyan. 2023

[13]The Application Status and Trends of Machine Vision in Tea Production. Zhiming Yang,Wei Ma,Jinzhu Lu,Zhiwei Tian,Kaiqian Peng. 2023

[14]Application of Machine Vision Technology in Citrus Production. Kaiqian Peng,Wei Ma,Jinzhu Lu,Zhiwei Tian,Zhiming Yang. 2023

[15]DESIGN AND IMPLEMENTATION OF THE POSITIONING AND DIRECTING PRECISION SEEDER FOR CUCURBITA FICIFOLIA SEEDS. Ding, Xinting,Chen, Tong,Wei, Yongzhe,He, Zhi,Cui, Yongjie,Yang, Qichang. 2024

[16]From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods. Junhua Lu,Mei Zhang,Yongsong Hu,Wei Ma,Zhiwei Tian,Hongsen Liao,Jiawei Chen,Yuxin Yang. 2024

[17]Rep-ViG-Apple: A CNN-GCN Hybrid Model for Apple Detection in Complex Orchard Environments. Bo Han,Ziao Lu,Jingjing Zhang,Rolla Almodfer,Zhengting Wang,Wei Sun,Luan Dong. 2024

[18]A Machine Vision-Based Method of Impurity Detection for Rapeseed Harvesters. Xu Chen,Zhuohuai Guan,Haitong Li,Min Zhang. 2024

[19]YOLOv5DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs. Wenhui Shi,Xiaopin Wang,Xuan Li,Yuhua Fu,Xiaolei Liu,Haiyan Wang. 2025

[20]The Application of an Intelligent Agaricus bisporus-Harvesting Device Based on FES-YOLOv5s. Hao Ma,Yulong Ding,Hongwei Cui,Jiangtao Ji,Xin Jin,Tianhang Ding,Jiaoling Wang. 2025

作者其他论文 更多>>