The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides
The design of a prototype decision support system (DSS) for growing malting barley (Hordeum vulgre L.) without use of pesticides is described. The system comprises a main module and two sub modules. The main module predicts the yield and some quality parameters, assuming no problems with weeds, fungal diseases, insect pests and harvest/post harvest conditions. The two sub-modules handle, respectively, the effects of fungal diseases and weeds and of mechanical weed control. The DSS is constructed as a Bayesian network and thus incorporates and presents information on uncertainties. The paper describes the principles of a Bayesian network and the principles used to calculate the conditional probabilities required in it. The design of the DSS is presented. An example of the use of the prototype and of the output is shown and it is explained how that information can be used. The benefits and some drawbacks of using a Bayesian network to construct a DSS are discussed.
bayesian network dss conditional probabilities expert knowledge hugin