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A digital weed counting system for the weed control performance evaluation

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dc.contributor.authorPamornnak, Burawich
dc.contributor.authorScholz, Christian
dc.contributor.authorBecker, Silke
dc.contributor.authorRuckelshausen, Arno
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.accessioned2022-02-24T13:34:45Z
dc.date.available2022-02-24T13:34:45Z
dc.date.issued2022
dc.description.abstractThe weed counting method is one of the keys to indicate the performance of the weed control process. This article presents a digital weed counting system to use instead of a conventional manual counting system called “Göttinger Zähl- und Schätzrahmen” or “Göttinger Rahmen” due to the limitation of human counting on big-scale field experiment areas. The proposed method demonstrated on the maize field consists of two main parts, a virtual weed counting frame and a weed counting core, respectively. The system was implemented as a mobile application for the smartphone (Android) with server-based processing. The pre-processed image on the mobile phone will be sent to the weed counting core based on the pre-trained convolution neural network model (CNN or deep learning) on the server. Finally, the number of detected weeds will be sent back to the mobile phone to show the results. In the first implementation, 100 frames on a 1-hectare field area were evaluated. The absolute weed counting errors were categorized into three groups, A-Group (0-10 weeds error) achieves 73 %, B-Group (11-20 weeds error) achieves 17 %, and C-Group (21-30 weeds error) achieves 10 %, respectively. For overall performance, the system achieves the  = 0.97 from the correlation and 12.8 % counting error. These results show the digital version of “Göttinger Rahmen” has the potential to become a practical tool for weed control evaluations.en
dc.identifier.isbn978-3-88579-711-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/38398
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.subjectGöttinger Rahmen
dc.subjectweed counting
dc.subjectmobile application
dc.subjectfield experiment
dc.subjectimage processing
dc.subjectdata labeling
dc.subjectdeep learning
dc.titleA digital weed counting system for the weed control performance evaluationen
dc.typeText/Conference Paper
gi.citation.endPage212
gi.citation.publisherPlaceBonn
gi.citation.startPage207
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