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Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks

2021-11-20Journal article Open access
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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorMiragaia, Rolando
dc.contributor.authorChávez, Francisco
dc.contributor.authorDíaz, Josefa
dc.contributor.authorVivas, Antonio
dc.contributor.authorPrieto, Maria Henar
dc.contributor.authorMoñino, Maria José
dc.date.accessioned2026-01-12T10:32:21Z
dc.date.available2026-01-12T10:32:21Z
dc.date.issued2021-11-20
dc.description.abstractDigitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.eng
dc.description.sponsorshipFunding: This research is part of Grant PID2020-115570GB-C21 funded by MCIN/AEI/10.13039/501100011033 and Regional Government of Extremadura, Department of Commerce and Economy, the European Regional Development Fund, A Way to Build Europe, under project IB16035 and Junta de Extremadura. Acknowledgments: We acknowledge the support of Grant PID2020-115570GB-C21 funded by MCIN/AEI/10.13039/501100011033, project AGROS and Regional Government of Extremadura, Department of Commerce and Economy, the European Regional Development Fund, A Way to Build Europe, under project IB16035.
dc.identifier.citationMiragaia, R., Chávez, F., Díaz, J., Vivas, A., Prieto, M. H., & Moñino, M. J. (2021). Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks. Agronomy, 11(11), 2353. https://doi.org/10.3390/agronomy11112353.
dc.identifier.doi10.3390/agronomy11112353
dc.identifier.eissn2073-4395
dc.identifier.urihttp://hdl.handle.net/10400.8/15284
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/2073-4395/11/11/2353
dc.relation.ispartofAgronomy
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectagriculture digitalization
dc.subjectprecision agriculture
dc.subjectcomputer vision
dc.subjectplum orchard
dc.subjectPrunus salicina
dc.titlePlum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networkseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage26
oaire.citation.issue11
oaire.citation.startPage1
oaire.citation.titleAgronomy Journal
oaire.citation.volume11
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMiragaia
person.givenNameRolando
person.identifier.ciencia-idC712-E02E-0ED2
person.identifier.orcid0000-0003-4213-9302
person.identifier.ridGLS-3615-2022
person.identifier.scopus-author-id26422369700
relation.isAuthorOfPublicationc3934650-8cbe-40cd-bb29-31c57baa49e2
relation.isAuthorOfPublication.latestForDiscoveryc3934650-8cbe-40cd-bb29-31c57baa49e2

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Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.
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