Abstract
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.
Highlights
Compared with the growing interest in plant phenotyping using computer vision and image analysis, plant phenotyping by visual inspection is slow and subjective, relying as it does on human evaluation
To achieve optimal plant segmentation, the natural question to first pose is which colour space is the more effective for the detection of plant pixels? Is there a suitable transformation of {Red Green Blue} (RGB) colour space to a representation that will make plant pixel detection more accurate and more reliable? Can a suitable representation be found that will improve the degree to which plant pixel detection is independent of illumination condition? Does the contrast between plant and background naturally get enhanced in certain colour space irrespective of illumination condition? These are some of the questions we address in this paper
The results of applying the proposed Earth Mover Distance (EMD) measure, Equation (8), on the Gaussian Mixture Models (GMMs) of foreground and background pixels from training data are shown in Tables 1 and 2
Summary
Compared with the growing interest in plant phenotyping using computer vision and image analysis, plant phenotyping by visual inspection is slow and subjective, relying as it does on human evaluation. Two of the aims of digital imaging and image analysis are (a) the removal of any degree of subjectivity associated with an individual human’s perception, and (b) the expedition of the analysis procedure. This is especially important for the high throughput assessment of the phenotypic manifestations of genetic expressions in plants. Another and particular application of digital imaging in an agricultural setting is the detection and identification of weeds for the purpose of spot spraying of herbicide.
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