Computers and Electronics in Agriculture 112 (2015) 10–19
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Computers and Electronics in Agriculture
journal homepage: www.elsevier.com/locate/compag
Detecting creeping thistle in sugar beet fields using vegetation indices
Wajahat Kazmi a,⇑, Francisco Jose Garcia-Ruiz b,1, Jon Nielsen b,1, Jesper Rasmussen b,1,
Hans Jørgen Andersen a,2
a
b
Department of Architecture, Design and Media Technology, Aalborg University, Rendsburggade 14, Room: 5 353, 9000 Aalborg, Denmark
Department of Plant and Environmental Sciences, Højbakkegård Allé 9, University of Copenhagen, 2630 Taastrup, Denmark
a r t i c l e
i n f o
Article history:
Received 5 April 2014
Received in revised form 3 January 2015
Accepted 8 January 2015
Available online 4 February 2015
Keywords:
Weed detection
Precision agriculture
Vegetation index
Sugar beet
Thistle
a b s t r a c t
In this article, we address the problem of thistle detection in sugar beet fields under natural, outdoor conditions. In our experiments, we used a commercial color camera and extracted vegetation indices from
the images. A total of 474 field images of sugar beet and thistles were collected and divided into six different groups based on illumination, scale and age. The feature set was made up of 14 indices. Mahalanobis Distance (MD) and Linear Discriminant Analysis (LDA) were used to classify the species. Among the
features, excess green (ExG), green minus blue (GB) and color index for vegetation extraction (CIVE)
offered the highest average accuracy, above 90%. The feature set was reduced to four important indices
following a PCA analysis, but the classification accuracy was similar to that obtained by only combining
ExG and GB which was around 95%, still better than an individual index. Stepwise linear regression
selected nine out of 14 features and offered the highest accuracy of 97%. The results of LDA and MD were
fairly close, making them both...