{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:43:47Z","timestamp":1777891427615,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ20F050006"],"award-info":[{"award-number":["LQ20F050006"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LGF20F050004"],"award-info":[{"award-number":["LGF20F050004"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62105085"],"award-info":[{"award-number":["62105085"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LQ20F050006"],"award-info":[{"award-number":["LQ20F050006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LGF20F050004"],"award-info":[{"award-number":["LGF20F050004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62105085"],"award-info":[{"award-number":["62105085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.<\/jats:p>","DOI":"10.3390\/s22207764","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"7764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks"],"prefix":"10.3390","volume":"22","author":[{"given":"Kaihua","family":"Wei","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bojian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejian","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1361-0663","authenticated-orcid":false,"given":"Hongze","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing 312000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.3934\/mbe.2021171","article-title":"Research on the influence of attention and emotion of tea drinkers based on artificial neural network","volume":"18","author":"Hong","year":"2021","journal-title":"Math. 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