Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing
A small field robot was designed and built within the framework of the H2020 project Xylella fastidiosa Active Containment Through a Multidisciplinary-Oriented Research Strategy (XF-ACTORS). The robot is remotely driven and provided with different proximal sensing equipment for the early detection o...
| Autores principales: | , , , , , , |
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| Formato: | conferenceObject |
| Lenguaje: | Inglés |
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Wageningen University & Research
2021
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/7573 https://edepot.wur.nl/471679#page=286 |
| _version_ | 1855032631161257984 |
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| author | López, Santiago Cubero, Sergio Aleixos, Nuria Alegre, Vicente Rey, Beatriz Aguilar, Enrique Blasco, José |
| author2 | Groot Koerkamp, Peter |
| author_browse | Aguilar, Enrique Alegre, Vicente Aleixos, Nuria Blasco, José Cubero, Sergio Groot Koerkamp, Peter López, Santiago Rey, Beatriz |
| author_facet | Groot Koerkamp, Peter López, Santiago Cubero, Sergio Aleixos, Nuria Alegre, Vicente Rey, Beatriz Aguilar, Enrique Blasco, José |
| author_sort | López, Santiago |
| collection | ReDivia |
| description | A small field robot was designed and built within the framework of the H2020 project Xylella fastidiosa Active Containment Through a Multidisciplinary-Oriented Research Strategy (XF-ACTORS). The robot is remotely driven and provided with different proximal sensing equipment for the early detection of Xf in olive groves, including thermal, colour and multispectral cameras, and a 2D laser scanner (LiDAR) to obtain the 3D structure of the crop. The equipment is completed by a GPS to geolocate the data obtained and an IMU (inertial measurement unit) to correct the data captured by the LiDAR. An industrial computer triggers the sensors and controls the data acquisition, which is synchronised with the advance of the robot by means of a pulse encoder coupled to the axis of the motor. Then, crop maps can be created off-line after the analysis of the collected data to show graphically potential Xf infection in the trees. Owing to the height of the olive trees inspected, the cameras were placed on a platform that can be elevated up to 200 cm. Two batteries power the electric motors attached to the wheels, thereby allowing a continuous inspection for approximately six hours (a field of about 4 ha). A series of tests have been carried out in an olive orchard showing slight symptoms of Xf infection in the region of Apulia, southern Italy. During the first tests, the robot inspected each row in both directions with the cameras pointing to one side, so as to inspect all sides of the trees. The tests were mainly focused on the development of the mechanics, navigation systems, sensors and data acquisition. Synchronised and geolocated images of the whole crop were also captured with the cameras in different climatic conditions, as well as with the laser scanner for later comparison to the in-situ observations |
| format | conferenceObject |
| id | ReDivia7573 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Wageningen University & Research |
| publisherStr | Wageningen University & Research |
| record_format | dspace |
| spelling | ReDivia75732025-04-25T14:52:00Z Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing López, Santiago Cubero, Sergio Aleixos, Nuria Alegre, Vicente Rey, Beatriz Aguilar, Enrique Blasco, José Groot Koerkamp, Peter Robotics LiDAR Asymptomatic detection H20 Plant diseases N20 Agricultural machinery and equipment N01 Agricultural engineering U30 Research methods Computer vision Multispectral imagery A small field robot was designed and built within the framework of the H2020 project Xylella fastidiosa Active Containment Through a Multidisciplinary-Oriented Research Strategy (XF-ACTORS). The robot is remotely driven and provided with different proximal sensing equipment for the early detection of Xf in olive groves, including thermal, colour and multispectral cameras, and a 2D laser scanner (LiDAR) to obtain the 3D structure of the crop. The equipment is completed by a GPS to geolocate the data obtained and an IMU (inertial measurement unit) to correct the data captured by the LiDAR. An industrial computer triggers the sensors and controls the data acquisition, which is synchronised with the advance of the robot by means of a pulse encoder coupled to the axis of the motor. Then, crop maps can be created off-line after the analysis of the collected data to show graphically potential Xf infection in the trees. Owing to the height of the olive trees inspected, the cameras were placed on a platform that can be elevated up to 200 cm. Two batteries power the electric motors attached to the wheels, thereby allowing a continuous inspection for approximately six hours (a field of about 4 ha). A series of tests have been carried out in an olive orchard showing slight symptoms of Xf infection in the region of Apulia, southern Italy. During the first tests, the robot inspected each row in both directions with the cameras pointing to one side, so as to inspect all sides of the trees. The tests were mainly focused on the development of the mechanics, navigation systems, sensors and data acquisition. Synchronised and geolocated images of the whole crop were also captured with the cameras in different climatic conditions, as well as with the laser scanner for later comparison to the in-situ observations 2021-08-25T15:48:00Z 2021-08-25T15:48:00Z 2018 conferenceObject López, S., Cubero, S., Aleixos, N., Alegre, V., Rey, B., Aguilar, E. & Blasco, J. (2018). Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing. Proceedings of the European Conference on Agricultural Engineering (AgEng2018), 286-290. http://hdl.handle.net/20.500.11939/7573 10.18174/471679 https://edepot.wur.nl/471679#page=286 en 2018-07 European Conference on Agricultural Engineering (AgEng2018) Wageningen, the Netherlands This work was partially supported by funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS). Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess Wageningen University & Research electronico |
| spellingShingle | Robotics LiDAR Asymptomatic detection H20 Plant diseases N20 Agricultural machinery and equipment N01 Agricultural engineering U30 Research methods Computer vision Multispectral imagery López, Santiago Cubero, Sergio Aleixos, Nuria Alegre, Vicente Rey, Beatriz Aguilar, Enrique Blasco, José Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing |
| title | Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing |
| title_full | Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing |
| title_fullStr | Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing |
| title_full_unstemmed | Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing |
| title_short | Preliminary approach for the detection of olive trees infected by Xylella fastidiosa using a field robot and proximal sensing |
| title_sort | preliminary approach for the detection of olive trees infected by xylella fastidiosa using a field robot and proximal sensing |
| topic | Robotics LiDAR Asymptomatic detection H20 Plant diseases N20 Agricultural machinery and equipment N01 Agricultural engineering U30 Research methods Computer vision Multispectral imagery |
| url | http://hdl.handle.net/20.500.11939/7573 https://edepot.wur.nl/471679#page=286 |
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