{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:17:12Z","timestamp":1781194632803,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.<\/jats:p>","DOI":"10.3390\/s23052471","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T04:32:33Z","timestamp":1677126753000},"page":"2471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties"],"prefix":"10.3390","volume":"23","author":[{"given":"Mikel","family":"Barrio-Conde","sequence":"first","affiliation":[{"name":"Departamento de Teor\u00eda de la Se\u00f1al y Comunicaciones e Ingenier\u00eda Telem\u00e1tica, Universidad de Valladolid, ETSI Telecomunicaci\u00f3n, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7306-7976","authenticated-orcid":false,"given":"Marco Antonio","family":"Zanella","sequence":"additional","affiliation":[{"name":"Agricultural Engineering Department, Federal University of Lavras, P.O. Box 3037, Lavras 37200-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7054-0369","authenticated-orcid":false,"given":"Javier Manuel","family":"Aguiar-Perez","sequence":"additional","affiliation":[{"name":"Departamento de Teor\u00eda de la Se\u00f1al y Comunicaciones e Ingenier\u00eda Telem\u00e1tica, Universidad de Valladolid, ETSI Telecomunicaci\u00f3n, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruben","family":"Ruiz-Gonzalez","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Escuela Polit\u00e9cnica Superior, University of Burgos, Avda. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3333-0148","authenticated-orcid":false,"given":"Jaime","family":"Gomez-Gil","sequence":"additional","affiliation":[{"name":"Departamento de Teor\u00eda de la Se\u00f1al y Comunicaciones e Ingenier\u00eda Telem\u00e1tica, Universidad de Valladolid, ETSI Telecomunicaci\u00f3n, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Increasing the Impact of Science and Technology to Provide More People with Healthier and Safer Food","volume":"10","author":"Thiry","year":"2021","journal-title":"Food Energy Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1016\/S0140-6736(20)30677-2","article-title":"Fertility, Mortality, Migration, and Population Scenarios for 195 Countries and Territories from 2017 to 2100: A Forecasting Analysis for the Global Burden of Disease Study","volume":"396","author":"Vollset","year":"2020","journal-title":"Lancet"},{"key":"ref_3","unstructured":"(2022, February 08). 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