Weed-crop discrimination using LiDAR measurements

D. Andújar; H. Moreno; C. Valero; R. Gerhards; H.W. Griepentrog

Weed-crop discrimination using LiDAR measurements

In: Precision agriculture '13

Authors:

D. Andújar

D. Andújar ¹University of Hohenheim, Institute of Phytomedicine, Section Weed Science (360b), Otto-Sander-Straße 5, 70593 Stuttgart, Germany; andujar@uni-hohenheim.de

Search for other papers by D. Andújar in
Current site
Google Scholar
PubMed

H. Moreno

H. Moreno ³University of Hohenheim, Institute of Agricultural Engineering, SectionInstrumentation & Test Engineering (440c),Garbenstraße 9, 70599 Stuttgart, Germany

Search for other papers by H. Moreno in
Current site
Google Scholar
PubMed

C. Valero

C. Valero ²Universidad Politécnica de Madrid, E.T.S.I. Agrónomos, Departamento de Ingenieria Rural, Madrid, Spain

Search for other papers by C. Valero in
Current site
Google Scholar
PubMed

R. Gerhards

R. Gerhards ¹University of Hohenheim, Institute of Phytomedicine, Section Weed Science (360b), Otto-Sander-Straße 5, 70593 Stuttgart, Germany; andujar@uni-hohenheim.de

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

, and

H.W. Griepentrog

H.W. Griepentrog ³University of Hohenheim, Institute of Agricultural Engineering, SectionInstrumentation & Test Engineering (440c),Garbenstraße 9, 70599 Stuttgart, Germany

Search for other papers by H.W. Griepentrog in
Current site
Google Scholar
PubMed

Type:: Chapter

Pages:: 541–545

DOI:: https://doi.org/10.3920/9789086867783_068

Purchase instant access (PDF download and unlimited online access):

€42.00€35.00 excl. VAT

Add to Cart

Redeem Access Token

In this paper, we propose a new approach for discriminating maize and weed plants from soil surface, evaluating the accuracy and performance of a LiDAR sensor for vegetation detection using distance and reflection values. Field measurements were conducted in a maize field at growth stage BBCH 12-14. Static measurements were taken at different sampling areas with different weed densities. Regression analyses were carried out to assess the capabilities of the system for vegetation and soil measurement. A high relationship between LiDAR measured distance (LiDAR heights) and actual height was found. A binary logistic regression was used to predict the presence or absence of vegetation. The results permitted the discrimination of vegetation from the soil with accuracy up to 95%. This technique offers significant promise for the development of real-time spatially selective weed control techniques, either as the sole weed detection system or in combination with other detection tools.

Citation Info

Save
Cite
Email this content

Share link with colleague or librarian

You can email a link to this page to a colleague or librarian:
Email this content
or copy the link directly:

https://brill.edhh.ma/abstract/book/9789086867783/BP000068.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Precision agriculture '13

E-Book ISBN:: 9789086867783

Publisher:: Wageningen Academic

Print Publication Date:: 01 Nov 2013

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

Metrics

	All Time	Past 365 days	Past 30 Days
Abstract Views	97	34	4
Full Text Views	2	0	0
PDF Views & Downloads	0	0	0

African Studies	Education	Media Studies
American Studies	History	Middle East and Islamic Studies
Ancient Near East and Egypt	Human Rights and Humanitarian Law	Musicology
Art History	International Law	Philosophy
Asian Studies	International Relations	Religious Studies
Biblical Studies	Jewish Studies	Slavic and Eurasian Studies
Biology	Languages and Linguistics	Social Sciences
Book History and Cartography	Life Sciences	Theology and World Christianity
Classical Studies	Literature and Cultural Studies

Subjects

Authors

Open Access

Product Information

Company

Contact

Weed-crop discrimination using LiDAR measurements

Citation Info

Share link with colleague or librarian

Precision agriculture '13

Table of Contents

Metrics

Metrics