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Artificial Intelligence Decision Support System Based on Artificial Neural Networks to Predict the Commercialization Time by the Evolution of Peach Quality

2021Journal article Open access
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dc.contributor.authorAnanias, Estevão
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorSoares, Vasco N. G. J.
dc.contributor.authorCaldeira, João M. L. P.
dc.date.accessioned2022-03-28T11:12:30Z
dc.date.available2022-03-28T11:12:30Z
dc.date.issued2021
dc.description.abstractClimacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical‐chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support Sys‐ tem (DSS) designed to predict the evolution of the quality of peaches, namely the storage time re‐ quired before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and val‐ idation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple predic‐ tion of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and con‐ sumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI‐based solutions for smart cities.pt_PT
dc.description.sponsorshipThis study is within the activities of project PrunusPós—Otimização de processos de ar‐ mazenamento, conservação em frio, embalamento ativo e/ou inteligente, e rastreabilidade da qual‐ idade alimentar no póscolheita de produtos frutícolas (Optimization of processes of storage, cold conservation, active and/or intelligent packaging, and traceability of food quality in the postharvest of fruit products), Operation n.º PDR2020‐101‐031695 (Partner), Consortium n.º 87, Initiative n.º 175 promoted by PDR2020 and co‐financed by FEADER under the Portugal 2020 initiative.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/electronics10192394pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/12121
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationInstituto de Telecomunicações
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/10/19/2394pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPeachpt_PT
dc.subjectPhysical-chemical Parameterspt_PT
dc.subjectRefrigeration Environmentpt_PT
dc.subjectQualitypt_PT
dc.subjectRetailpt_PT
dc.subjectArtificial intelligence decision support systempt_PT
dc.subjectSmart Citiespt_PT
dc.titleArtificial Intelligence Decision Support System Based on Artificial Neural Networks to Predict the Commercialization Time by the Evolution of Peach Qualitypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.issue19pt_PT
oaire.citation.startPage2394pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume10pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameGaspar
person.familyNameda Gama de Jesus Soares
person.familyNameCaldeira
person.givenNamePedro Dinis
person.givenNameVasco Nuno
person.givenNameJoão
person.identifiera4GD8aoAAAAJ
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id5B19-E130-E382
person.identifier.ciencia-idA91B-85B8-C27E
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-8057-5474
person.identifier.orcid0000-0001-5830-3790
person.identifier.ridN-3016-2013
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id27067580500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationb69e2ba0-43af-4cf7-873e-090fd9fc6c94
relation.isAuthorOfPublication2e3b8027-c291-4cb7-bcc2-7f9b065a4eb3
relation.isAuthorOfPublication431a7461-e862-4b66-86ea-684e274030bf
relation.isAuthorOfPublication.latestForDiscovery431a7461-e862-4b66-86ea-684e274030bf
relation.isProjectOfPublication5a9bd4c8-57a9-46c4-95dc-a5e5c220c117
relation.isProjectOfPublication.latestForDiscovery5a9bd4c8-57a9-46c4-95dc-a5e5c220c117

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