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

Abstract:

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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