Alternative Non-Destructive Approach for Estimating Morphometric Measurements of Chicken Eggs from Tomographic Images with Computer Vision

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal Rural University of Pernambuco (UFRPE)-
Autor(es): dc.creatorLópez Vargas, Jean Pierre Brik-
Autor(es): dc.creatorde Abreu, Katariny Lima-
Autor(es): dc.creatorDuarte de Paula, Davi-
Autor(es): dc.creatorPinheiro Salvadeo, Denis Henrique-
Autor(es): dc.creatorArantes de Souza, Lilian Francisco-
Autor(es): dc.creatorBôa-Viagem Rabello, Carlos-
Data de aceite: dc.date.accessioned2025-08-21T19:40:16Z-
Data de disponibilização: dc.date.available2025-08-21T19:40:16Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/foods13244039-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301420-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301420-
Descrição: dc.descriptionThe egg has natural barriers that prevent microbiological contamination and promote food safety. The use of non-destructive methods to obtain morphometric measurements of chicken eggs has the potential to replace traditional invasive techniques, offering greater efficiency and accuracy. This paper aims to demonstrate that estimates derived from non-invasive approaches, such as 3D computed tomography (CT) image analysis, can be comparable to conventional destructive methods. To achieve this goal, two widely recognized deep learning architectures, U-Net 3D and Fully Convolutional Networks (FCN) 3D, were modeled to segment and analyze 3D CT images of chicken eggs. A dataset of real CT images was created and labeled, allowing the extraction of important morphometric measurements, including height, width, shell thickness, and volume. The models achieved an accuracy of up to 98.69%, demonstrating their effectiveness compared to results from manual measurements. These findings highlight the potential of CT image analysis, combined with deep learning, as a non-invasive alternative in industrial and research settings. This approach not only minimizes the need for invasive procedures but also offers a scalable and reliable method for egg quality assessment.-
Descrição: dc.descriptionFundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco-
Descrição: dc.descriptionInstitute of Geosciences and Exact Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionZootechnics Department Federal Rural University of Pernambuco (UFRPE), PE-
Descrição: dc.descriptionInstitute of Geosciences and Exact Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionFundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco: 037850422-
Idioma: dc.languageen-
Relação: dc.relationFoods-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subject3D image segmentation-
Palavras-chave: dc.subjectcomputer tomographic images-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjecteggs quality-
Palavras-chave: dc.subjectmorphometric data extraction-
Palavras-chave: dc.subjectpoultry-
Título: dc.titleAlternative Non-Destructive Approach for Estimating Morphometric Measurements of Chicken Eggs from Tomographic Images with Computer Vision-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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