Combining deep learning and X-ray imaging technology to assess tomato seed quality

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dc.creatorHerika Paula Pessoa
dc.creatorMariane Gonçalves Ferreira Copati
dc.creatorAlcinei Mistico Azevedo
dc.creatorFrançoise Dalprá Dariva
dc.creatorGabriella Queiroz de Almeida
dc.creatorCarlos Nick Gomes
dc.date.accessioned2024-09-17T15:27:46Z
dc.date.accessioned2025-09-09T00:12:25Z
dc.date.available2024-09-17T15:27:46Z
dc.date.issued2023
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1590/1678-992X-2022-0121
dc.identifier.issn1678-992X
dc.identifier.urihttps://hdl.handle.net/1843/76548
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofScientia Agricola
dc.rightsAcesso Aberto
dc.subjectTomate
dc.subjectGerminação
dc.subjectSementes -- Qualidade
dc.subjectInteligência artificial
dc.subjectProdutividade agrícola
dc.subject.otherTomate
dc.subject.otherGerminação
dc.subject.otherSementes -- Qualidade
dc.subject.otherInteligência artificial
dc.subject.otherProdutividade agrícola
dc.titleCombining deep learning and X-ray imaging technology to assess tomato seed quality
dc.typeArtigo de periódico
local.citation.epage10
local.citation.spage1
local.citation.volume80
local.description.resumoTraditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.scielo.br/j/sa/a/PCT4vR4fHkKY9Pb9B3wY6tK/?format=pdf&lang=en

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