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Fruit recognition and classification based on SVM method for production prediction of peaches – Preliminary study

2020Conference object Restricted access
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dc.contributor.authorPereira, Tiago
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorSimões, M.P.
dc.date.accessioned2020-10-23T14:47:48Z
dc.date.available2020-10-23T14:47:48Z
dc.date.issued2020
dc.description.abstractThe concept of Precision Agriculture is usually associated with the usage of high-end technology equipment (hardware or software) to evaluate or monitor the conditions of a determined portion of land, adjusting afterwards the production factors, like seeds, fertilizers, pesticides, growing regulators, water, according to differential detected characteristics. This paper describes an algorithm developed to analyze and process images to recognize fruits, particularly peaches, and calculate it dimensions, like volume and weight. The recognition of peaches on their natural conditions on trees depends on several spatial- and time-variable parameters and requires complex segmentation algorithms. The proposed algorithm applies image segmentation for extraction of characteristics such as color and shape. These characteristics were used to train a classification method through a Support Vector Machine (SVM) to improve the recognition rate of fruits. The algorithm is designed to acquire images with a high-resolution camera installed in a drone that will fly between the tree lines. The production prediction of 29.3 tons per hectare was obtained based on volume and relation weight/volume calculated for the recognized peaches. An overall precision of 72% was achieved for the prediction rate of peaches in orchards (808 trees/ha). This is the first study regarding the application of these concepts under orchard trees aiming the production prediction along the fruit maturation. Other useful future applications are foreseen in orchard trees, related not only to production prediction, for this type of algorithm.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPEREIRA, T.M. ; GASPAR, P.D. ; SIMÕES, M.P. (2020) - Fruit recognition and classification based on SVM method for production prediction of peaches - preliminary study. In IV Balkan Symposium on Fruit Growing. Acta Hortic. 1289, p. 141-150. DOI: https://doi.org/10.17660/ActaHortic.2020.1289.21pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/7287
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherISHSpt_PT
dc.relationPrunusBOT (PDR2020)pt_PT
dc.relationAgroI9 (PDR2020)pt_PT
dc.relation.ispartofseries1289
dc.subjectPrecision agriculturept_PT
dc.subjectSupport vector machinept_PT
dc.subjectProduction predictionpt_PT
dc.subjectFruit detectionpt_PT
dc.subjectPrunus persicapt_PT
dc.titleFruit recognition and classification based on SVM method for production prediction of peaches – Preliminary studypt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceIstanbul, Turquiapt_PT
oaire.citation.endPage150pt_PT
oaire.citation.startPage141pt_PT
oaire.citation.titleActa Horticulturaept_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.embargofctA revista Acta Horticulturae só permite o acesso a sócio da ISHSpt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationebfd94b1-21cd-4670-8626-e82f2b1c3436
relation.isAuthorOfPublicationc1c2eaaf-223e-4152-9245-04303ee41d75
relation.isAuthorOfPublication.latestForDiscoveryebfd94b1-21cd-4670-8626-e82f2b1c3436

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