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Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

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Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

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dc.contributor.author Rey, Beatriz es_ES
dc.contributor.author Aleixos Borrás, María Nuria es_ES
dc.contributor.author Cubero-García, Sergio es_ES
dc.contributor.author Blasco Ivars, Jose es_ES
dc.date.accessioned 2020-04-24T07:14:21Z
dc.date.available 2020-04-24T07:14:21Z
dc.date.issued 2019 es_ES
dc.identifier.issn 2072-4292 es_ES
dc.identifier.uri http://hdl.handle.net/10251/141459
dc.description.abstract [EN] The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations. es_ES
dc.description.sponsorship This work was partially supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Remote Sensing es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Robotics es_ES
dc.subject Computer vision es_ES
dc.subject Multispectral imaging es_ES
dc.subject LiDAR es_ES
dc.subject Pest detection aid es_ES
dc.subject Vegetative indices es_ES
dc.subject Asymptomatic detection es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.title Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/rs11030221 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/727987/EU/Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària es_ES
dc.description.bibliographicCitation Rey, B.; Aleixos Borrás, MN.; Cubero-García, S.; Blasco Ivars, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). https://doi.org/10.3390/rs11030221 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/rs11030221 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\385886 es_ES
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