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Peaches detection using a deep learning technique — A contribution to yield estimation resources management, and circular economy.

2022Journal article Open access
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dc.contributor.authorAssunção, Eduardo
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorMesquita, Ricardo
dc.contributor.authorSimões, M.P.
dc.contributor.authorRamos, António Santos
dc.contributor.authorProença, Hugo
dc.contributor.authorInácio, Pedro R.M.
dc.date.accessioned2022-02-22T11:17:26Z
dc.date.available2022-02-22T11:17:26Z
dc.date.issued2022
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.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAssunção, E.T.; Gaspar, P.D.; Mesquita, R.J.M.; Simões, M.P.; Ramos, A.; Proença, H.; Inácio, P.R.M. Peaches Detection Using a Deep Learning Technique—A Contribution to Yield Estimation, Resources Management, and Circular Economy. Climate 2022, 10, 11. https://doi.org/10.3390/ cli10020011pt_PT
dc.identifier.doi10.3390pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/7904
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/pt_PT
dc.subjectConvutional 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 economy.pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleClimatept_PT
oaire.citation.volume10pt_PT
person.familyNameGaspar
person.familyNameSimões
person.givenNamePedro Dinis
person.givenNameMaria Paula
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id5215-A196-0362
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-6599-0688
person.identifier.ridN-3016-2013
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id36504886200
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationebfd94b1-21cd-4670-8626-e82f2b1c3436
relation.isAuthorOfPublicationc1c2eaaf-223e-4152-9245-04303ee41d75
relation.isAuthorOfPublication.latestForDiscoveryebfd94b1-21cd-4670-8626-e82f2b1c3436

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