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Navigation system of autonomous multitask robotic rover for agricultural activities on peach orchards based on computer vision through tree trunk detection

2022Journal article Restricted access
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dc.contributor.authorSimões, J.P.
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorAssunção, Eduardo
dc.contributor.authorMesquita, Ricardo
dc.contributor.authorSimões, M.P.
dc.date.accessioned2023-01-16T15:06:30Z
dc.date.available2023-01-16T15:06:30Z
dc.date.issued2022
dc.description.abstractIntroducing robotics in agriculture can allow a rise in productivity and a reduction in costs and waste. Its capabilities can be enhanced to or above the human level, enabling a robot to function as a human does, but with higher precision, repeatability, and with little to no effort. This paper develops a detection algorithm of peach trunks in orchard rows, as autonomous navigation and anti-bump auxiliary system of a terrestrial robotic rover for agricultural applications. The approach involved computational vision, more specifically, the creation of an object detection model based on Convolutional Neural Networks. The framework of the algorithm is Tensorflow, for implementation in a Raspberry Pi 4. The model’s core is the detection system SSD MobileNet 640×640 with transfer learning from the COCO 2017 database. 89 pictures were captured for the database of the model, of which 90% were used for training and the other 10% for testing. The model was converted for mobile applications with a full integer quantization, from 32floatto uint8, and it was compiled for Edge TPU support. The orientation strategy consists of two conditions: a double detection forms a linear function, represented by an imaginary line, which updates every two simultaneous trunks detected. Through the slope of this function and the horizontal deviation of a single detected bounding box from the created line, the algorithm orders the robot to adjust the orientation or keep moving forward. The arithmetic evaluation of the model shows a precision and recall of 94.4%. After the quantization, the new values of these metrics are 92.3 and 66.7%, respectively. These simulation results prove that, statistically, the model can perform the navigation task.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSIMÕES, J.P. [et al.] (2022) - Navigation system of autonomous multitask robotic rover for agricultural activities on peach orchards based on computer vision through tree trunk detection. Acta Horticulturae. 1352, p. 593-600. DOI: 10.17660/ActaHortic.2022.1352.80pt_PT
dc.identifier.doi10.17660/ActaHortic.2022.1352.80pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/8284
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherISHSpt_PT
dc.relationPDR2020-101-031358pt_PT
dc.subjectPrecision agriculturept_PT
dc.subjectObject detectionpt_PT
dc.subjectOrchardpt_PT
dc.subjectNavigationpt_PT
dc.subjectTerrestrial roverpt_PT
dc.subjectRobotic visionpt_PT
dc.subjectCNNpt_PT
dc.subjectTensorflowpt_PT
dc.subjectRaspberry Pi 4pt_PT
dc.subjectSSD MobileNetpt_PT
dc.subjectQuantizationpt_PT
dc.titleNavigation system of autonomous multitask robotic rover for agricultural activities on peach orchards based on computer vision through tree trunk detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage600pt_PT
oaire.citation.issue1352pt_PT
oaire.citation.startPage593pt_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.rightsrestrictedAccesspt_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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