Investigation of the effect of hectoliter and thousand grain weight on variety identification in wheat using deep learning method

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dc.authoridLUY, Murat/0000-0002-2378-0009
dc.authoridTURK, FUAT/0000-0001-8159-360X
dc.contributor.authorLuy, Murat
dc.contributor.authorTurk, Fuat
dc.contributor.authorArgun, Mustafa Samil
dc.contributor.authorPolat, Turgay
dc.date.accessioned2025-01-21T16:42:02Z
dc.date.available2025-01-21T16:42:02Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAccurate identification of wheat varieties in the seed and flour industry is extremely important. The success rate of correctly identifying wheat varieties using artificial intelligence methods compared to traditional methods is quite high. Whether hectoliter weight (HLW) and thousand grain weight (TGW) represent the variety in iden-tification studies is a subject to debate. The reason of this debate is these parameters are heavily affected by environmental factors such as soil nutrient levels, amount of rainfall, and number of sunny days. In other words, it is assumed that these parameters are not specific to the variety. In this study, the feature map obtained using the GLCM method was compared with the feature map obtained by adding the HLW and TGW parameters. As a result of the comparison, the accuracy rate was calculated as 78% in the first feature map. However, when standard features were added to the HLW and TGW parameters, the accuracy rate was calculated as 82%. The results show that the HLW and TGW parameters contribute to the identification of the wheat variety when used correctly with artificial intelligence.
dc.identifier.doi10.1016/j.jspr.2023.102116
dc.identifier.issn0022-474X
dc.identifier.issn1879-1212
dc.identifier.urihttps://doi.org/10.1016/j.jspr.2023.102116
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25000
dc.identifier.volume102
dc.identifier.wosWOS:000986016000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofJournal of Stored Products Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectWheat variety identification; Hectoliter weight; Thousand grain weight; Deep learning; Gray level Co-Occurrence matrix
dc.titleInvestigation of the effect of hectoliter and thousand grain weight on variety identification in wheat using deep learning method
dc.typeArticle

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