{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:12:11Z","timestamp":1784301131073,"version":"3.55.0"},"reference-count":72,"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\/"}],"funder":[{"name":"Polish Ministry of Science","award":["005\/RID\/2018\/19"],"award-info":[{"award-number":["005\/RID\/2018\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models\u2019 validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.<\/jats:p>","DOI":"10.3390\/s23052486","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T02:03:26Z","timestamp":1677204206000},"page":"2486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7223-6491","authenticated-orcid":false,"given":"Piotr","family":"Rybacki","sequence":"first","affiliation":[{"name":"Department of Agronomy, Faculty of Agronomy, Horticulture and Bioengineering, Pozna\u0144 University of Life Sciences, Dojazd 11, 60-632 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8011-9487","authenticated-orcid":false,"given":"Janetta","family":"Niemann","sequence":"additional","affiliation":[{"name":"Department of Genetics and Plant Breeding, Faculty of Agronomy, Horticulture and Bioengineering, Pozna\u0144 University of Life Sciences, Dojazd 11, 60-632 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2208-2946","authenticated-orcid":false,"given":"Kiril","family":"Bahcevandziev","sequence":"additional","affiliation":[{"name":"Agricultural College of Coimbra (ESAC\/IPC), Research Centre for Natural Resources, Environment and Society (CERNAS), 3045-601 Coimbra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4811-005X","authenticated-orcid":false,"given":"Karol","family":"Durczak","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Pozna\u0144 University of Life Sciences, Wojska Polskiego 50, 60-637 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.tifs.2020.03.044","article-title":"Oilseeds beyond oil: Press cakes and meals supplying global protein requirements","volume":"100","author":"Arrutia","year":"2020","journal-title":"Trends Food Sci. 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