Automatic derivation of weed densities from images for site-specific weed management

M. Weis; R. Gerhards

Automatic derivation of weed densities from images for site-specific weed management

in Precision agriculture '09

Autor:innen:

M. Weis

M. Weis Department of Weed Science, University of Hohenheim, Germany; Martin.Weis@uni-hohenheim.de

Search for other papers by M. Weis in
Current site
Google Scholar
PubMed

und

R. Gerhards

R. Gerhards Department of Weed Science, University of Hohenheim, Germany; Martin.Weis@uni-hohenheim.de

Search for other papers by R. Gerhards in
Current site
Google Scholar
PubMed

Art:: Kapitel

Seiten:: 349–354

DOI:: https://doi.org/10.1163/9789086866649_042

Über Institut einloggen

Sofortzugang erwerben (PDF-Download und unbegrenzter Online-Zugang):

42,00 €35,00 € excl. VAT

Add to Cart

Token einlösen

Abstract

Site-specific herbicide applications can save large amounts of herbicides and improve management practices. One crucial part of a system for site-specific weed management is the measurement of the spatial variability of weed densities. A system was developed to identify different weed species from images taken in the field. The automation has the potential to increase the spatial density of weed sampling points. A manual sampling with high density of points is unfeasible due to the costs. Image processing algorithms are used to generate a shape description for each plant in the image. A classifier can be constructed that assigns weed and crop classes to the plants based on the shape features. Weed density maps are generated using the results of the classification. The weed maps are transformed to application maps, which are used for the site-specific herbicide application. The shape of the plants vary with their growth stage and may be segmented into parts, e.g. single leaves, in the image processing. Therefore different classes for each species need to be introduced into the process. To avoid over- or underestimation of the actual number of weeds some of the classes are aggregated using weight factors. To derive transformation functions the results of the automatically derived weed counts are compared to manual measurements of weed densities, which were derived from the images and in the field. The results show that the raw classification results are linearly related to the actual number of manually counted weeds and the results can be used as input for a site-specific decision component.

Zitierungsangaben

Save
Cite
Inhalt per Mail versenden

Link teilen

Sie können einen Link zu dieser Seite per E-Mail senden:
Inhalt per Mail versenden
oder den Link direkt kopieren:

https://brill.edhh.ma/abstract/book/9789086866649/B9789086866649_s042.xml
Der Link wurde nicht kopiert. Ihr aktueller Browser unterstützt das Kopieren über diese Schaltfläche möglicherweise nicht.

Link wurde kopiert

Reduzieren
Erweitern
Nach oben

Precision agriculture '09

ISBN:: 9789086866649

Verleger:: Wageningen Academic

Print-Publikationsdatum:: 01 Jan 2009

Fachgebiete
- Biowissenschaften
  - Allgemein
  - Landwirtschaft und Umwelt

Front Matter

Show Items

Back Matter

Show Items

Kennzahlen

	Insgesamt	Letzte 365 Tage	In den letzten 30 Tagen
Aufrufe von Kurzbeschreibungen	39	25	6
Gesamttextansichten	0	0	0
PDF-Downloads	0	0	0

Afrikanistik	Internationale Beziehungen	Nahost- und Islamwissenschaften
Altorientalistik und Ägyptologie	Internationales Recht	Pädagogik
Amerikanistik	Judaistik	Philosophie
Asienwissenschaften	Klassische Altertumswissenschaft	Religionswissenschaft
Bibelauslegung	Kunstgeschichte	Slawistik und Eurasienkunde
Biologie	Literatur- und Kulturwissenschaften	Sozialwissenschaften
Biowissenschaften	Medienwissenschaft	Sprachen und Linguistik
Buchgeschichte und Kartographie	Menschenrechte und humanitäres Völkerrecht	Theologie und Christentum
Geschichte	Musikwissenschaft

Fachgebiete

Autor:innen

Open Access

Produkt-Informationen

Über uns

Kontakt

Automatic derivation of weed densities from images for site-specific weed management

Abstract

Zitierungsangaben

Link teilen

Precision agriculture '09

Inhaltsverzeichnis

Kennzahlen

Kennzahlen