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Embedding a real-time strawberry detection model into a pesticide-spraying mobile robot for greenhouse operation

2024-08-15Artigo científico Acesso aberto
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datacite.subject.sdg12:Produção e Consumo Sustentáveis
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorAmraoui, Khalid El
dc.contributor.authorAnsari, Mohamed El
dc.contributor.authorLghoul, Mouataz
dc.contributor.authorAlaoui, Mustapha El
dc.contributor.authorAbanay, Abdelkrim
dc.contributor.authorJabri, Bouazza
dc.contributor.authorMasmoudi, Lhoussaine
dc.contributor.authorLUÍS VALENTE DE OLIVEIRA, JOSÉ
dc.date.accessioned2026-01-19T10:30:58Z
dc.date.available2026-01-19T10:30:58Z
dc.date.issued2024-08-15
dc.description.abstractAbstract: The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 × 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices.eng
dc.identifier.doi10.3390/app14167195
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.1/28139
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.relation.ispartofApplied Sciences
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAgricultural robotics
dc.subjectReal-time detection
dc.subjectYOLO
dc.subjectEmbedded system
dc.subjectGreenhouse
dc.titleEmbedding a real-time strawberry detection model into a pesticide-spraying mobile robot for greenhouse operationeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.citation.issue16
oaire.citation.startPage7195
oaire.citation.titleApplied Sciences
oaire.citation.volume14
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameJOSÉ
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0001-5337-5699
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublicationbb726e73-690c-4a33-822e-c47bdac3035b
relation.isAuthorOfPublication.latestForDiscoverybb726e73-690c-4a33-822e-c47bdac3035b
relation.isProjectOfPublication1122b3d4-9740-4ad7-9abf-86bb7a3615da
relation.isProjectOfPublication.latestForDiscovery1122b3d4-9740-4ad7-9abf-86bb7a3615da

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