Detection rate and spraying accuracy of Ecorobotix ARA
Zusammenfassung
Machine Learning enabled the long hoped-for breakthrough in the field of automated single-plant weed control. Ecorobotix ARA (Ecorobotix, Yverdon, Switzerland) was the first commercially available spot-sprayer allowing automated single-plant detection and control of broad-leaved dock (Rumex obtusifolius) in meadows. Cameras are used to record the vegetation and machine learning-based algorithms detect the plants in real time. This makes it possible to selectively treat only the target plants. The aim of the present research was to investigate the accuracy of the detection and spraying of the plants in comparison to manual treatment with a knapsack sprayer. With a detection rate of over 85 % in most cases and a slightly better spraying accuracy compared to manual treatment, this first spot sprayer for meadows showed a good performance in practical use.
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- BibTeX
Anken, T. & Latsch, A.,
(2022).
Detection rate and spraying accuracy of Ecorobotix ARA.
In:
Gandorfer, M., Hoffmann, C., El Benni, N., Cockburn, M., Anken, T. & Floto, H.
(Hrsg.),
42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft.
Bonn:
Gesellschaft für Informatik e.V..
(S. 45-50).
@inproceedings{mci/Anken2022,
author = {Anken, Thomas AND Latsch, Annett},
title = {Detection rate and spraying accuracy of Ecorobotix ARA},
booktitle = {42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft},
year = {2022},
editor = {Gandorfer, Markus AND Hoffmann, Christa AND El Benni, Nadja AND Cockburn, Marianne AND Anken, Thomas AND Floto, Helga} ,
pages = { 45-50 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
author = {Anken, Thomas AND Latsch, Annett},
title = {Detection rate and spraying accuracy of Ecorobotix ARA},
booktitle = {42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft},
year = {2022},
editor = {Gandorfer, Markus AND Hoffmann, Christa AND El Benni, Nadja AND Cockburn, Marianne AND Anken, Thomas AND Floto, Helga} ,
pages = { 45-50 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
| Dateien | Groesse | Format | Anzeige | |
|---|---|---|---|---|
| GIL2022_Anken_45-50.pdf | 199.0Kb | Öffnen |
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Mehr Information
ISBN: 978-3-88579-711-1
ISSN: 1617-5468
Datum: 2022
Sprache:
(en)
(en)
Typ: Text/Conference Paper

