Logo des Repositoriums
 
Konferenzbeitrag
Full Review

A comparative study of RGB and multispectral imaging for weed detection in precision agriculture

Lade...
Vorschaubild

Volltext URI

Dokumententyp

Text/Conference Paper

Zusatzinformation

Datum

2024

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

Gesellschaft für Informatik e.V.

Zusammenfassung

Precision agriculture and specifically mechanical weed control systems have the potential to positively impact our environment by reducing the use of herbicides. In recent years, multispectral cameras have become more and more accessible, which raises the question whether the additional costs of such cameras are worth the potential benefits. In this study, we recorded and annotated a multispectral instance segmentation dataset for sugar beet crop and weed detection. We trained Mask-RCNN models on the RGB and multispectral data in a transfer learning approach and extensively evaluated and compared the results for different scenarios. We found that the multispectral data can improve the weed detection performance significantly in many cases.

Beschreibung

Benedikt Fischer, Pascal Gauweiler (2024): A comparative study of RGB and multispectral imaging for weed detection in precision agriculture. 44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft. DOI: 10.18420/giljt2024_60. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-738-8. pp. 227-232. Stuttgart. 27.-28. Februar 2024

Zitierform

Tags