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Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular Economy

2022-01-18Journal article Open access
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dc.contributor.authorAssunção, Eduardo Timóteo
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorMesquita, Ricardo
dc.contributor.authorSimões, Maria Paula
dc.contributor.authorRamos, António
dc.contributor.authorProença, H.
dc.contributor.authorInácio, Pedro R. M.
dc.date.accessioned2022-03-30T09:01:32Z
dc.date.available2022-03-30T09:01:32Z
dc.date.issued2022-01-18
dc.description.abstractFruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.pt_PT
dc.description.sponsorshipThe authors are thankful to Fundação para a Ciência e Tecnologia (FCT) and R&D Unit “Center for Mechanical and Aerospace Science and Technologies” (C-MAST), under project UIDB/00151/2020, for the opportunity and the financial support to carry on this project. The contributions of Hugo Proença and Pedro Inácio in this work were supported by FCT/MEC through FEDER—PT2020 Partnership Agreement under Project UIDB//50008/2021.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/cli10020011pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/12133
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCentre for Mechanical and Aerospace Science and Technologies
dc.relationInstituto de Telecomunicações
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectConvolutional neural networkpt_PT
dc.subjectDeep learningpt_PT
dc.subjectFruit detectionpt_PT
dc.subjectPrecision agriculturept_PT
dc.subjectSustainabilitypt_PT
dc.titlePeaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular Economypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Mechanical and Aerospace Science and Technologies
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00151%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.issue2pt_PT
oaire.citation.startPage11pt_PT
oaire.citation.titleClimatept_PT
oaire.citation.volume10pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAssunção
person.familyNameGaspar
person.familyNameMesquita
person.familyNameSimões
person.familyNameRamos
person.familyNameProença
person.familyNameInácio
person.givenNameEduardo Timóteo
person.givenNamePedro Dinis
person.givenNameRicardo
person.givenNameMaria Paula Albuquerque Figueiredo
person.givenNameAntónio
person.givenNameHugo
person.givenNamePedro
person.identifier1153590
person.identifier.ciencia-id421E-B6CA-E3A1
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id5215-A196-0362
person.identifier.ciencia-idED16-81E7-0319
person.identifier.ciencia-idBF1D-BCA4-179F
person.identifier.orcid0000-0001-6027-7763
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-8599-6737
person.identifier.orcid0000-0002-6599-0688
person.identifier.orcid0000-0002-3041-0196
person.identifier.orcid0000-0003-2551-8570
person.identifier.orcid0000-0001-8221-0666
person.identifier.ridN-3016-2013
person.identifier.ridF-9499-2010
person.identifier.ridI-1778-2019
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id14016540600
person.identifier.scopus-author-id27168727400
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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