Leaf segmentation and parallel phenotyping for the analysis of gene networks in plants

Over the last 4 years phenotyping is becoming more and more automated, decreasing a lot of manual labour. Features, which uniquely define the plant, can be extracted automatically from images. As a lot of plant data has to be processed in order to extract the features, fast processing of these features is a challenge. Therefore in this paper, a new method for automatic segmentation of individual leaves from plants with a circular arrangement of leaves (rosettes) is proposed, together with an algorithm to extract the line of symmetry of the leaf. Furthermore, in order to achieve fast processing for phenotyping plants, four feature extraction methods are parallelised in order to run on the CPU and GPU. Our evaluation results show that by parallelizing the feature extraction methods, it is possible to calculate the image moments, area, histogram and sum of intensities 5 to 45 times faster than single threaded implementations.