{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:00:23Z","timestamp":1767772823841,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T00:00:00Z","timestamp":1678060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFF1000302","2021YFD1200700","BK20BE007"],"award-info":[{"award-number":["2021YFF1000302","2021YFD1200700","BK20BE007"]}]},{"name":"Foshan Science and Technology Innovation Fund, the University of Science and Technology Beijing (USTB)","award":["2021YFF1000302","2021YFD1200700","BK20BE007"],"award-info":[{"award-number":["2021YFF1000302","2021YFD1200700","BK20BE007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate real-time classification of fluorescently labelled maize kernels is important for the industrial application of its advanced breeding techniques. Therefore, it is necessary to develop a real-time classification device and recognition algorithm for fluorescently labelled maize kernels. In this study, a machine vision (MV) system capable of identifying fluorescent maize kernels in real time was designed using a fluorescent protein excitation light source and a filter to achieve optimal detection. A high-precision method for identifying fluorescent maize kernels based on a YOLOv5s convolutional neural network (CNN) was developed. The kernel sorting effects of the improved YOLOv5s model, as well as other YOLO models, were analysed and compared. The results show that using a yellow LED light as an excitation light source combined with an industrial camera filter with a central wavelength of 645 nm achieves the best recognition effect for fluorescent maize kernels. Using the improved YOLOv5s algorithm can increase the recognition accuracy of fluorescent maize kernels to 96%. This study provides a feasible technical solution for the high-precision, real-time classification of fluorescent maize kernels and has universal technical value for the efficient identification and classification of various fluorescently labelled plant seeds.<\/jats:p>","DOI":"10.3390\/s23052840","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T02:28:34Z","timestamp":1678069714000},"page":"2840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Zilong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Ben","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Zhong Zhi International Institute of Agricultural Biosciences, Beijing 101200, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Beijing 528300, China"}]},{"given":"Wenbo","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3335-0632","authenticated-orcid":false,"given":"Suowei","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Zhong Zhi International Institute of Agricultural Biosciences, Beijing 101200, China"},{"name":"School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Xuejie","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Hao","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5939-4847","authenticated-orcid":false,"given":"Xiangyuan","family":"Wan","sequence":"additional","affiliation":[{"name":"Beijing Zhong Zhi International Institute of Agricultural Biosciences, Beijing 101200, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Beijing 528300, China"},{"name":"School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Yong","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Zhong Zhi International Institute of Agricultural Biosciences, Beijing 101200, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Beijing 528300, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.5539\/jfr.v11n2p16","article-title":"Effect of Grain Moisture Content and Roller Mill Gap Size on Various Physical Properties of Yellow Dent Corn Flour","volume":"11","author":"Shafinas","year":"2022","journal-title":"J. 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