Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing
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...
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| Format: | Artículo |
| Language: | Inglés |
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MDPI
2019
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| Online Access: | http://hdl.handle.net/20.500.11939/6286 https://www.mdpi.com/2072-4292/11/3/221/htm |
| _version_ | 1855492053232779264 |
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| author | Rey, Beatriz Aleixos, Nuria Cubero, Sergio Blasco, José |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Rey, Beatriz |
| author_facet | Rey, Beatriz Aleixos, Nuria Cubero, Sergio Blasco, José |
| author_sort | Rey, Beatriz |
| collection | ReDivia |
| description | 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. |
| format | Artículo |
| id | ReDivia6286 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia62862025-04-25T14:46:45Z Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing Rey, Beatriz Aleixos, Nuria Cubero, Sergio Blasco, José Robotics computer vision multispectral imaging LiDAR pest detection aid vegetative indices asymptomatic detection xylella fastidiosa H20 Plant diseases Computer vision Plant disease control Olives 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. 2019-12-17T10:53:56Z 2019-12-17T10:53:56Z 2019 article publishedVersion Rey, B., Aleixos, N., Cubero, S., & Blasco, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing, 11(3), 221. 2072-4292 http://hdl.handle.net/20.500.11939/6286 10.3390/rs11030221 https://www.mdpi.com/2072-4292/11/3/221/htm en Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ MDPI electronico |
| spellingShingle | Robotics computer vision multispectral imaging LiDAR pest detection aid vegetative indices asymptomatic detection xylella fastidiosa H20 Plant diseases Computer vision Plant disease control Olives Rey, Beatriz Aleixos, Nuria Cubero, Sergio Blasco, José Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing |
| title | Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing |
| title_full | Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing |
| title_fullStr | Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing |
| title_full_unstemmed | Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing |
| title_short | Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing |
| title_sort | xf rovim a field robot to detect olive trees infected by xylella fastidiosa using proximal sensing |
| topic | Robotics computer vision multispectral imaging LiDAR pest detection aid vegetative indices asymptomatic detection xylella fastidiosa H20 Plant diseases Computer vision Plant disease control Olives |
| url | http://hdl.handle.net/20.500.11939/6286 https://www.mdpi.com/2072-4292/11/3/221/htm |
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