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Detection rate and spraying accuracy of Ecorobotix ARA

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dc.contributor.authorAnken, Thomas
dc.contributor.authorLatsch, Annett
dc.contributor.editorGandorfer, Markus
dc.contributor.editorHoffmann, Christa
dc.contributor.editorEl Benni, Nadja
dc.contributor.editorCockburn, Marianne
dc.contributor.editorAnken, Thomas
dc.contributor.editorFloto, Helga
dc.date.accessioned2022-02-24T13:34:55Z
dc.date.available2022-02-24T13:34:55Z
dc.date.issued2022
dc.description.abstractMachine 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.en
dc.identifier.isbn978-3-88579-711-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/38417
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-317
dc.subjectspot spraying
dc.subjectweed
dc.subjectmeadow
dc.subjectRumex obtusifolius
dc.subjectcomputer vision
dc.titleDetection rate and spraying accuracy of Ecorobotix ARAen
dc.typeText/Conference Paper
gi.citation.endPage50
gi.citation.publisherPlaceBonn
gi.citation.startPage45
gi.conference.date21.-22. Februar 2022
gi.conference.locationTänikon, Online

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P317 - 42. GIL-Jahrestagung 2022 - Fokus: Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft