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Analytical Study of Colour Spaces for Plant Pixel Detection

School of Information Technology and Mathematical Sciences; Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide SA 5001, Australia
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Imaging 2018, 4(2), 42;
Received: 26 September 2017 / Revised: 12 February 2018 / Accepted: 12 February 2018 / Published: 16 February 2018
(This article belongs to the Special Issue Color Image Processing)
Segmentation of regions of interest is an important pre-processing step in many colour image analysis procedures. Similarly, segmentation of plant objects in digital images is an important preprocessing step for effective phenotyping by image analysis. In this paper, we present results of a statistical analysis to establish the respective abilities of different colour space representations to detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation for which the separation of the distributions representing object and background pixels is maximized is the best for the detection of plant pixels. The two pixel classes are modelled by Gaussian Mixture Models (GMMs). In our statistical modelling we make no prior assumptions on the number of Gaussians employed. Instead, a constant bandwidth mean-shift filter is used to cluster the data with the number of clusters, and hence the number of Gaussians, being automatically determined. We have analysed the following representative colour spaces: R G B , r g b , H S V , Y c b c r and C I E - L a b . We have analysed the colour space features from a two-class variance ratio perspective and compared the results of our model with this metric. The dataset for our empirical study consisted of 378 digital images (and their manual segmentations) of a variety of plant species: Arabidopsis, tobacco, wheat, and rye grass, imaged under different lighting conditions, in either indoor or outdoor environments, and with either controlled or uncontrolled backgrounds. We have found that the best segmentation of plants is found using H S V colour space. This is supported by measures of Earth Mover Distance (EMD) of the GMM distributions of plant and background pixels. View Full-Text
Keywords: plant phenotyping; plant pixel classification; colour space; Gaussian Mixture Model; Earth Mover Distance; variance ratio; plant segmentation plant phenotyping; plant pixel classification; colour space; Gaussian Mixture Model; Earth Mover Distance; variance ratio; plant segmentation
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Kumar, P.; Miklavcic, S.J. Analytical Study of Colour Spaces for Plant Pixel Detection. J. Imaging 2018, 4, 42.

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