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Real-time detection of vine trunk for robot localization using deep learning models developed for edge TPU devices

2022Journal article Open access
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dc.contributor.authorAlibabaei, Khadijeh
dc.contributor.authorAssunção, Eduardo
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorSoares, V.N.G.J.
dc.contributor.authorCaldeira, J.M.L.P.
dc.date.accessioned2022-07-04T09:58:07Z
dc.date.available2022-07-04T09:58:07Z
dc.date.issued2022
dc.description.abstractThe concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationALIBABAEI, Khadijeh [et al.] (2022) - Real-time detection of vine trunk for robot localization using deep learning models developed for edge TPU devices. Future Internet. DOI 10.3390/fi14070199
dc.identifier.doi10.3390/fi14070199pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/8015
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationFCT/MCTES through national funds and, when applicable, co-funded EU funds under project UIDB/EEA/50008/2020pt_PT
dc.subjectagriculturept_PT
dc.subjectdeep learningpt_PT
dc.subjectIOTpt_PT
dc.subjectrobotpt_PT
dc.subjecttrunk detectionpt_PT
dc.titleReal-time detection of vine trunk for robot localization using deep learning models developed for edge TPU devicespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue7pt_PT
oaire.citation.startPage199pt_PT
oaire.citation.titleFuture Internetpt_PT
oaire.citation.volume14pt_PT
person.familyNameGaspar
person.familyNameCaldeira
person.givenNamePedro Dinis
person.givenNameJoão
person.identifiera4GD8aoAAAAJ
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id5B19-E130-E382
person.identifier.ciencia-idA91B-85B8-C27E
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-8057-5474
person.identifier.orcid0000-0001-5830-3790
person.identifier.ridN-3016-2013
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id27067580500
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationebfd94b1-21cd-4670-8626-e82f2b1c3436
relation.isAuthorOfPublicationa17d4ff5-1ff3-4dcc-b180-319e7ff3961d
relation.isAuthorOfPublication8eebc97c-5334-4f29-b7ee-71c4c436aa69
relation.isAuthorOfPublication.latestForDiscoverya17d4ff5-1ff3-4dcc-b180-319e7ff3961d

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