Abstract
This paper describes the modeling of a biomass based weed-crop competitiveness classification process, based on a Bayesian network classifier. The understandability of the model is improved by its automatic translation into a set of classification rules, which are easily understood by human beings. The Bayes approach is based on empirical data collected in a corn-crop and uses the concept of maximum a posteriori probability to extract a set of probabilistic rules from the induced Bayesian network classifier. The features used to build the Bayesian network classifier are the total density of weeds and the corresponding proportions of narrow and broadleaf weeds and the class variable is the weeds biomass from which the weed-crop competitiveness is inferred. The paper presents a set of 27 rules extracted from the Bayesian network classifier which classify the biomass of weeds.
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