Presentation on theme: "Machine Vision Applications Case Study No. 3 Analysing Images of Living Plants: Visual control of a robot for micropropagation."— Presentation transcript:
Machine Vision Applications Case Study No. 3 Analysing Images of Living Plants: Visual control of a robot for micropropagation
Micropropagation –Rapid non-fruiting copying of genetically identical plants –How micropropagation is performed Human beings cannot do it properly –Plants are fragile –People infect growing medium Blind robots cannot do it –Visually guided robot could Plant variation and structure –Intra-species variation –Open and closed plant structures
Open Structure Plant (Rose) Planted in agar jelly Bifurcation: leaf stalk attached to main stem (nodal bud growing) Replant Y-shaped piece Leaves, stalks and stem are all discarded
Lighting and Viewing Back-lighting (red to increase contrast) Two or three cameras, horizontal view Take care to avoid stray light
Preprocessing Enhance contrast and threshold –Follow with binary closing operator Crack detector –Applied to grey-scale image –Finds stem and stalks; removes leaves from the image)
Locating Bifurcations Skeletonising / thinning –Find joints by counting white 8-neighbours Morphology / N-tuple – V-shaped structuring element / kernel Place “pastry cutter” on each bifurcation –Cutting uses a high-power scanning laser (minimal plant damage)
Handling Occlusion Birfucations identified from any camera –OR results from all cameras –Interpolate from one view to another when tracking the main stem - needs intelligence –Ineffective for closed plant structures
Additional Measurements Height Width Symmettry and other aesthetic shape parameters Compactness (“leggy” plants are weak) Total leaf area - determines plant growth Leaf size distribution Stem / stalk widthr (measures physical strength) Leaf droop (indicates need for watering)
Reading B. G. Batchelor, “Intelligent Image Processing in Prolog”, Springer-Verlag, Berlin & New York, 1991, ISBN 0-540-19647-1 & ISBN 0-387- 19647-1, § 7.7. M. Graves & B. G. Batchelor, “Machine Vision for the Inspection of Natural Products “, Springer Verlag, London, 2004, ISBN 1-852-33525-4, § 3.5.2.
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