Micropropagation of plants is done by cutting juvenile plants and placing them into special container-boxes with nutrient-solution where the pieces can grow up and be cut again several times. To produce high amounts of biomass it is necessary to do plant micropropagation by a robotic syshoot. In this paper we describe parts of the vision syshoot that recognizes plants and their particular cutting points. Therefore, it is necessary to extract elements of the plants and relations between these elements (for example root, shoot, leaf). Different species vary in their morphological appearance, variation is also immanent in plants of the same species. Therefore, we introduce several morphological classes of plants from that we expect same recognition methods. As a result of our work we present rules which help users to create specific algorithms for object recognition of plant species.
Automatic micropropagation is necessary to produce cost-effective high amounts of biomass. Juvenile plants are dissected in clean- room environment on particular points on the stem or the leaves. A vision-system detects possible cutting points and controls a specialized robot. This contribution is directed to the pattern- recognition algorithms to detect structural parts of the plant.
The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.