Identification and Shape Analysis of Arabidopsis Cultivated in Nitrogen-free Environment

This paper presents a method for segmentation and shape description of Arabidopsis plants with nongreen leaves. The image was first calibrated by detecting the corners of a checkerboard. After the preprocessing step, the image was transformed to CIELUV color space, removing the lightness from the chromatic coordinates. The U component showed markedly different textures between the plant and the background. Hence its standard derivation was calculated and thresholded. With this method, significant leaves of the plant were separated while some stalks were not. Therefore, Support Vector Machine was then used to train the LUV data to do further segmentation as a complement of texture analysis. With these two steps, the plant was completely identified and the shape features were then extracted, including the total area, the symmetry and the number of leaves. The real area of the plant was derived with the number of foreground pixels and the calibration result. The symmetries were represented with the degrees of bilateral symmetry in the direction of the major and minor axes. And the number of leaves was obtained by identifying the number of local maximum of the contour-based signature. Experiment result shows that this method is effective in segmentation and shape analysis of Arabidopsis plants. Copyright © 2014 IFSA Publishing, S. L.