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Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study

cris.lastimport.scopus2025-10-23T06:58:50Z
cris.virtual.author-orcid0000-0002-2222-6866
cris.virtual.author-orcid0000-0002-5461-9170
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cris.virtualsource.author-orcid09c81af2-c2b2-424c-b2ec-4cda600883bf
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dc.abstract.enStarch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorPiekutowska, Magdalena
dc.contributor.authorHara, Patryk
dc.contributor.authorPentoś, Katarzyna
dc.contributor.authorLenartowicz, Tomasz
dc.contributor.authorWojciechowski, Tomasz
dc.contributor.authorKujawa, Sebastian
dc.contributor.authorNiedbała, Gniewko
dc.date.access2025-01-14
dc.date.accessioned2025-01-14T12:38:44Z
dc.date.available2025-01-14T12:38:44Z
dc.date.copyright2024-12-18
dc.date.issued2024
dc.description.abstract<jats:p>Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,3
dc.description.number12
dc.description.points100
dc.description.versionfinal_published
dc.description.volume14
dc.identifier.doi10.3390/agronomy14123010
dc.identifier.issn2073-4395
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2346
dc.identifier.weblinkhttps://www.mdpi.com/2073-4395/14/12/3010
dc.languageen
dc.pbn.affiliationmechanical engineering
dc.relation.ispartofAgronomy
dc.relation.pagesart. 3010
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.enstarch content
dc.subject.enpotato
dc.subject.enprediction
dc.subject.enartificial neural networks
dc.subject.enmultiple linear regression
dc.titlePredicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue12
oaire.citation.volume14