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Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

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Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

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dc.contributor.author Oblitas, Jimy es_ES
dc.contributor.author Mejía, Jezreel es_ES
dc.contributor.author De-La-Torre, Miguel es_ES
dc.contributor.author Avila-George, Himer es_ES
dc.contributor.author Seguí Gil, Lucía es_ES
dc.contributor.author Mayor López, Luis es_ES
dc.contributor.author Ibarz, Albert es_ES
dc.contributor.author Castro, Wilson es_ES
dc.date.accessioned 2021-03-10T04:31:16Z
dc.date.available 2021-03-10T04:31:16Z
dc.date.issued 2021-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/163576
dc.description.abstract [EN] Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques¿Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)¿when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1¿score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Applied Sciences es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Cucurbita pepo L. es_ES
dc.subject Image processing es_ES
dc.subject Micrograph es_ES
dc.subject Plant tissue es_ES
dc.subject CNN es_ES
dc.subject RBNN es_ES
dc.subject.classification TECNOLOGIA DE ALIMENTOS es_ES
dc.subject.classification INGENIERIA QUIMICA es_ES
dc.title Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/app11041581 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Química y Nuclear - Departament d'Enginyeria Química i Nuclear es_ES
dc.description.bibliographicCitation Oblitas, J.; Mejía, J.; De-La-Torre, M.; Avila-George, H.; Seguí Gil, L.; Mayor López, L.; Ibarz, A.... (2021). Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks. Applied Sciences. 11(4):1-13. https://doi.org/10.3390/app11041581 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/app11041581 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 4 es_ES
dc.identifier.eissn 2076-3417 es_ES
dc.relation.pasarela S\429076 es_ES


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