{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:41:42Z","timestamp":1783003302564,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Study on optical properties of spectral vision systems","award":["74130-71010023"],"award-info":[{"award-number":["74130-71010023"]}]},{"name":"Sun Yat-Sen University","award":["74130-71010023"],"award-info":[{"award-number":["74130-71010023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu\u2019er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu\u2019er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.<\/jats:p>","DOI":"10.3390\/rs16091590","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T04:01:52Z","timestamp":1714449712000},"page":"1590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Design of a Multimodal Detection System Tested on Tea Impurity Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7545-4365","authenticated-orcid":false,"given":"Zhankun","family":"Kuang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6673-3221","authenticated-orcid":false,"given":"Xiangyang","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Nanchang Research Institute, Sun Yat-sen University, Nanchang 330096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yefan","family":"Cai","sequence":"additional","affiliation":[{"name":"Guangzhou Guangxin Technology Co., Ltd., Guangzhou 510300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibin","family":"Hong","sequence":"additional","affiliation":[{"name":"Guangzhou Guangxin Technology Co., Ltd., Guangzhou 510300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1007\/s42979-022-01653-5","article-title":"Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques","volume":"4","author":"Mukhi","year":"2023","journal-title":"SN Comput. 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