Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Keywords

Agricultural zones, Bayesian network classifiers, Classification rules, Crop fields, Fuzzy classification systems, Fuzzy rule set, Kriging, Risk predictions, Weed infestation, Weed seed, Yield loss, Bayesian networks, Competition, Inference engines, Risk perception