Application of a Practical Approach for Incorporating Trust and Certainty of Information into a Knowledge Processing System in the Agricultural Domain

In knowledge processing systems, data is gathered from several sources, in the system, some calculating and processing steps are taken, and finally a result is computed and may be used for further steps or other systems. Most of the time the origin of input data is not verified. Using unverified data may cause inconsistencies in processing and generating output, and could lead to corrupting threats for the system and the environment. We propose an approach, where several characterizing values in a system - trust of source and certainty (and importance) of data - are used to compute new output characteristics of a knowledge processing system. These values should represent the trustworthiness and the certainty of the output in multi-step processing systems, based on all used sources and input data. We also apply the approach in a used calculation model in the agricultural domain: the Disease Pressure Model, which predicts the potential outbreak of a disease on a special field.