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Peach tree canopy assessment through aerial image detection for improved management and sustainability

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dc.contributor.authorSimões, M.P.
dc.contributor.authorVeloso, Abel
dc.contributor.authorAssunção, E.
dc.contributor.authorMoreira, E.
dc.contributor.authorTeixeira, M.C.C.
dc.contributor.authorGaspar, Pedro Dinis
dc.date.accessioned2025-06-27T12:48:10Z
dc.date.available2025-06-27T12:48:10Z
dc.date.issued2025
dc.description.abstractPrecision agriculture aims to detect differences in plant development within adefined area, allowing to adjust production techniques according to the plantdevelopment. Plant development is evaluated by remote detection allowing theapplication of variable rate application techniques. The efficiency of precisionagriculture is correlated with large areas of the same crop where plantsdevelopment is highly correlated with soil characteristics variation. Theapplication of precision agriculture in fruit production is common in the speciesthat allow mechanical harvesting, and, consequently, allows obtaining yieldmaps. Peach production is carried out by manual harvesting and is usuallybased on medium-sized parcels, as the fruit is very perishable and harvestwindow for each cultivar is short, from 10 to 15 days. This paper describes theexperimental tests developed to evaluate the volume of peach tree canopy,based on images captured by a drone, and its correlation with trunk sectionarea, which is a common way of assessing tree vigor. Ten plants per orchardwere monitored in 20 different orchards. The correlation between tree canopyand nutritional status was developed using the orchard as the repeating unit.The results indicate that projected canopy area, evaluated by aerial images, isdirectly correlated with tree canopy volume, in full developed orchards, as treeheight is standardized in every orchard according to the training system. Theremote assessment of the tree canopy allows the adoption of differentiatedtechniques that contribute to better management focused on a greater incometo the farmer and, at the same time, contribute to environmental sustainability.eng
dc.identifier.citationSIMÕES, M.P. [et al.] (2025) - Prunus persica; Tree canopy area; Trunk sectional area; Nutritional status; Aerial imaging; Image detection and classification. Acta Horticulturae 1433. DOI: 10.17660/ActaHortic.2025.1433.14
dc.identifier.doi10.17660/actahortic.2025.1433.14
dc.identifier.issn0567-7572
dc.identifier.issn2406-6168
dc.identifier.urihttp://hdl.handle.net/10400.11/10181
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInternational Society for Horticultural Science (ISHS)
dc.relation.ispartofActa Horticulturae
dc.rights.uriN/A
dc.subjectPrunus persica
dc.subjectTree canopy area
dc.subjectTrunk sectional area
dc.subjectNutritional status
dc.subjectAerial imaging
dc.subjectImage detection and classification
dc.titlePeach tree canopy assessment through aerial image detection for improved management and sustainability
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1433
oaire.citation.titleActa Horticulturae
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSimões
person.familyNameda Costa Oliveira Veloso Macedo
person.familyNameTeixeira
person.familyNameGaspar
person.givenNameMaria Paula
person.givenNameAbel
person.givenNameM.C.C.
person.givenNamePedro Dinis
person.identifier.ciencia-id5215-A196-0362
person.identifier.ciencia-id911D-446C-F79F
person.identifier.ciencia-id0913-BC21-F66E
person.identifier.ciencia-id6111-9F05-2916
person.identifier.orcid0000-0002-6599-0688
person.identifier.orcid0000-0002-2912-7246
person.identifier.orcid0000-0002-8534-9484
person.identifier.orcid0000-0003-1691-1709
person.identifier.ridN-3016-2013
person.identifier.scopus-author-id36504886200
person.identifier.scopus-author-id57419570900
relation.isAuthorOfPublicationc1c2eaaf-223e-4152-9245-04303ee41d75
relation.isAuthorOfPublicationa2693377-0fd4-4045-8e0f-af754815f8bb
relation.isAuthorOfPublication5c53ae6c-7865-4ef1-adea-ea2fa221aee4
relation.isAuthorOfPublicationebfd94b1-21cd-4670-8626-e82f2b1c3436
relation.isAuthorOfPublication.latestForDiscoverya2693377-0fd4-4045-8e0f-af754815f8bb

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