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Artificial Neural Network Model for Predicting Carrot Root Yield Loss in Relation to Mechanical Heading

cris.virtual.author-orcid0000-0001-7223-6491
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid0000-0001-5616-5697
cris.virtual.author-orcid0000-0002-3412-180X
cris.virtual.author-orcid0000-0003-1011-2551
cris.virtualsource.author-orcid110d6c25-5395-4f20-b8e7-5e160c853b52
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid786b69fe-001f-4f73-9560-6e7d2061db38
cris.virtualsource.author-orcid20d45583-0f3d-4625-895d-d154e808c225
cris.virtualsource.author-orcid79a3a1b4-e2cc-4896-9afa-85fbbb4824d1
dc.abstract.enModelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R2adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha−1 for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha−1. That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha−1) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface.
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Biotechnologii
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Agronomii
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorRybacki, Piotr
dc.contributor.authorPrzygodziński, Przemysław
dc.contributor.authorOsuch, Andrzej
dc.contributor.authorOsuch, Ewa
dc.contributor.authorKowalik, Ireneusz
dc.date.access2024-10-05
dc.date.accessioned2024-10-08T11:12:52Z
dc.date.available2024-10-08T11:12:52Z
dc.date.copyright2024-10-05
dc.date.issued2024
dc.description.bibliographyil., bibliogr.
dc.description.financeother
dc.description.financecost4 782,00
dc.description.if3,3
dc.description.number10
dc.description.points100
dc.description.versionfinal_published
dc.description.volume14
dc.identifier.doihttps:// doi.org/10.3390/agriculture14101755
dc.identifier.doihttps://www.mdpi.com/2077-0472/14/10/1755
dc.identifier.eissn2077-0472
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/1791
dc.languageen
dc.relation.ispartofAgriculture (Switzerland)
dc.relation.pagesart. 1755
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.enyield loss prediction
dc.subject.encarrot roots
dc.subject.enneural networks
dc.subject.enartificial intelligence
dc.subject.enmechanical heading
dc.titleArtificial Neural Network Model for Predicting Carrot Root Yield Loss in Relation to Mechanical Heading
dc.typeJournalArticle
dspace.entity.typePublication
project.funder.nameThe publication was co-financed by the Polish Minister of Science and Higher Education as part of the Strategy of the Poznan University of Life Sciences for 2024-2026 in the field of improving scientific research and development work in priority research areas.