Automated pattern recognition in crop plant phenotyping
Udo Seiffert
Fraunhofer Institute IFF Magdeburg, Biosystems Engineering


Breeding of novel crop plants has become increasingly important to address the challenges of growing earth population, climate change, and demand of regenerative energy (e.g. bio-fuels) as well as mineral oil substitutes (bio-polymers).
This requires crop plants with tailored and very specific properties. Regardless whether these plants are developed by classical plant breeding or direct biomolecular manipulation (GM plants), a statistically valid and high-throughput assessment of the plant's phenotype is always essential. In order to assess the relevant attributes of a plant's phenotype, typically the recognition and modelling of spatiotemporal development patterns needs to be done.
By means of a number of challenging real-world applications this talk will demonstrate how computational intelligence and machine learning assists this endeavour.

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