Detection of Hygiene-relevant Parameters from Cereal Grains based on Intelligent Image Interpretation and Data Mining

Petra Perner, Thomas Günther

Abstract

We are going on to develop a novel method for the detection of hygiene-relevant parameters from grains of cereal crops based on intelligent image acquisition and interpretation methods as well as data mining method. We present our first case study that describes the data acquisition, the planned image analysis and interpretation method as well as the reasoning methods that can map the automatic acquired parameters of grain to the relevant hygiene parameters. The prelimi-nary results show that with the new computer science methods it is possible to come up with new insights into the quality control of food stuff.

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