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Prediction of the Hemp Yield Using Artificial Intelligence Methods

cris.virtual.author-orcid0000-0003-0000-6157
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cris.virtualsource.author-orcid5e10caab-6ff8-471e-83cf-04cdbe8885b6
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dc.abstract.enThe aim of this study was to determine the usefulness of artificial neural networks (ANN) in the process of forecasting the yield of hemp seeds (Cannabis sativa L.) of the Henola variety. The field experiments (various doses of mineral fertilization, sowing date, row spacing) results were also used to generate neural models. The highest straw (15.90 Mg∙ha−1) and seed (2.93 Mg∙ha−1) yield were obtained for the highest dose of mineral fertilization and sowing date at the turn of April and May in Wielkopolska Region resulted in the highest yields of both straw (14.70 Mg∙ha−1) and seeds (2.66 Mg∙ha−1). As a result of the conducted research, two linear models of ANN s were generated. The 4: 8–1: 1 model, used to forecast the seed yield was characterized by an accuracy of nearly 91%, and the RMSPE error less than 34%. The second model, the 4: 4–1: 1 network, was used to forecast the straw yield and had The test quality nearly 74%, and the RMSPE error 26%.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorFrankowski, Jakub
dc.contributor.authorZaborowicz, Maciej
dc.contributor.authorSieracka, Dominika
dc.contributor.authorŁochyńska, Małgorzata
dc.contributor.authorCzeszak, Witold
dc.date.accessioned2024-11-25T09:59:16Z
dc.date.available2024-11-25T09:59:16Z
dc.date.issued2022
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,5
dc.description.number16
dc.description.points140
dc.description.reviewreview
dc.description.volume19
dc.identifier.doi10.1080/15440478.2022.2105468
dc.identifier.eissn1544-046X
dc.identifier.issn1544-0478
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2066
dc.languageen
dc.relation.ispartofJournal of Natural Fibers
dc.relation.pages13725-13735
dc.rightsClosedAccess
dc.sciencecloudsend
dc.subject.enhemp cultivation
dc.subject.enartificial neural networks
dc.subject.enparameters of affecting yield
dc.subject.enCannabis sativa
dc.subject.enhemp seed
dc.subject.enhemp straw
dc.subject.enyield forecasting
dc.titlePrediction of the Hemp Yield Using Artificial Intelligence Methods
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue16
oaire.citation.volume19