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Three-Dimensional Convolutional Neural Networks (3D-CNN) in the Classification of Varieties and Quality Assessment of Soybean Seeds (Glycine max L. Merill)

cris.lastimport.scopus2025-10-23T06:55:35Z
cris.virtual.author-orcid0000-0001-7223-6491
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dc.abstract.enThe precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992.
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Biotechnologii
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Agronomii
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.affiliation.instituteKatedra Genetyki i Hodowli Roślin
dc.contributor.authorRybacki, Piotr
dc.contributor.authorBahcevandziev, Kiril
dc.contributor.authorJarquin, Diego
dc.contributor.authorKowalik, Ireneusz
dc.contributor.authorOsuch, Andrzej
dc.contributor.authorOsuch, Ewa
dc.contributor.authorNiemann, Janetta
dc.date.access2025-08-29
dc.date.accessioned2025-08-29T06:32:45Z
dc.date.available2025-08-29T06:32:45Z
dc.date.copyright2025-08-28
dc.date.issued2025
dc.description.abstract<jats:p>The precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,4
dc.description.number9
dc.description.points100
dc.description.versionfinal_published
dc.description.volume15
dc.identifier.doi10.3390/agronomy15092074
dc.identifier.issn2073-4395
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4495
dc.identifier.weblinkhttps://www.mdpi.com/2073-4395/15/9/2074
dc.languageen
dc.pbn.affiliationagriculture and horticulture
dc.relation.ispartofAgronomy
dc.relation.pagesart. 2074
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.ensoybean
dc.subject.enseed quality
dc.subject.endigital phenotyping
dc.subject.en3D-CNN
dc.subject.enmachine learning
dc.titleThree-Dimensional Convolutional Neural Networks (3D-CNN) in the Classification of Varieties and Quality Assessment of Soybean Seeds (Glycine max L. Merill)
dc.title.volumeSpecial Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue9
oaire.citation.volume15