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Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains

cris.virtual.author-orcid0000-0002-9239-4072
cris.virtual.author-orcid0000-0001-5616-3827
cris.virtual.author-orcid0000-0001-6128-0315
cris.virtual.author-orcid0000-0003-1377-1878
cris.virtual.author-orcid0000-0002-7949-7560
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cris.virtualsource.author-orcid85dd240b-a6d1-4110-ab08-361ff2720cb6
cris.virtualsource.author-orcida2f42993-2b76-4d53-acc8-61c1b5b10c4e
cris.virtualsource.author-orcidab187d78-3916-499a-a077-9e8a0069cf71
cris.virtualsource.author-orcidb976ee79-488e-4f5b-a01c-f4c8be752932
cris.virtualsource.author-orcid0e1fa5f1-cce5-447d-b775-5c89abb28874
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThe paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Bioinżynierii
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.affiliation.instituteKatedra Inżynierii Wodnej i Sanitarnej
dc.affiliation.instituteKatedra Entomologii i Ochrony Åšrodowiska
dc.contributor.authorBoniecki, Piotr
dc.contributor.authorSujak, Agnieszka
dc.contributor.authorPilarska, Agnieszka
dc.contributor.authorPiekarska-Boniecka, Hanna
dc.contributor.authorWawrzyniak, Agnieszka
dc.contributor.authorRaba, Barbara
dc.date.access2026-01-30
dc.date.accessioned2026-02-10T08:43:54Z
dc.date.available2026-02-10T08:43:54Z
dc.date.copyright2022-08-31
dc.date.issued2022
dc.description.abstract<jats:p>The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,9
dc.description.number17
dc.description.points100
dc.description.versionfinal_published
dc.description.volume22
dc.identifier.doi10.3390/s22176578
dc.identifier.issn1424-8220
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7275
dc.identifier.weblinkhttps://www.mdpi.com/1424-8220/22/17/6578
dc.languageen
dc.relation.ispartofSensors
dc.relation.pagesart. 6578
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.endigital image
dc.subject.engraphic descriptors
dc.subject.enPCA (principal component analysis)
dc.subject.encompression of graphical data
dc.subject.enclassification of quality
dc.subject.enmalting barley
dc.titleDimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains
dc.title.volumeSpecial Issue Advances in Deep Learning for Intelligent Sensing Systems
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue17
oaire.citation.volume22