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Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root

cris.virtual.author-orcid0000-0001-7223-6491
cris.virtual.author-orcid0000-0002-7030-3221
cris.virtual.author-orcid0000-0002-0153-4624
cris.virtual.author-orcid0000-0001-5616-5697
cris.virtual.author-orcid0000-0003-4811-005X
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cris.virtualsource.author-orcid110d6c25-5395-4f20-b8e7-5e160c853b52
cris.virtualsource.author-orcidc958793f-eed3-43f8-afcc-10e1aa232f24
cris.virtualsource.author-orcid4ddc81ce-066b-4d2e-a9f3-015a6c34a525
cris.virtualsource.author-orcid786b69fe-001f-4f73-9560-6e7d2061db38
cris.virtualsource.author-orcidebe065e1-88e3-4328-92a9-62cb28d0570e
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThe main objective of the study is the development of an automatic carrot root classification model, marked as CR-NET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consistingof an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Biotechnologii
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Agronomii
dc.affiliation.instituteKatedra Technologii Żywności Pochodzenia Roślinnego
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorRybacki, Piotr
dc.contributor.authorSawinska, Zuzanna
dc.contributor.authorKačániová, Miroslava
dc.contributor.authorKowalczewski, Przemysław Łukasz
dc.contributor.authorOsuch, Andrzej
dc.contributor.authorDurczak, Karol
dc.date.access2025-04-10
dc.date.accessioned2025-06-26T07:45:57Z
dc.date.available2025-06-26T07:45:57Z
dc.date.copyright2024-03-15
dc.date.issued2024
dc.description.abstract<jats:p>The main objective of the study is the development of an automatic carrot root classification model, marked as CR-NET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consistingof an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if1,1
dc.description.number1
dc.description.points70
dc.description.versionfinal_published
dc.description.volume33
dc.identifier.doi10.23986/afsci.135986
dc.identifier.eissn1795-1895
dc.identifier.issn1459-6067
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2888
dc.identifier.weblinkhttps://journal.fi/afs/article/view/135986
dc.languageen
dc.relation.ispartofAgricultural and Food Science
dc.relation.pages40-54
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.enfood quality
dc.subject.enPython
dc.subject.enmachine learning
dc.subject.enCNN
dc.titleConvolutional neural network model for the qualitative evaluation of geometric shape of carrot root
dc.typeJournalArticle
dspace.entity.typePublication