Risk prediction for weed infestation using classification rules

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:24:03Z
dc.date.available2014-05-27T11:24:03Z
dc.date.created2014-05-27T11:24:03Z
dc.date.issued2009-12-01
dc.identifierProceedings of the IEEE International Conference on Control Applications, p. 1798-1803.
dc.identifierhttp://hdl.handle.net/11449/71282
dc.identifier10.1109/CCA.2009.5280694
dc.identifier2-s2.0-74049100180
dc.identifier6958497786939585
dc.description.abstractThis 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.
dc.languageeng
dc.relationProceedings of the IEEE International Conference on Control Applications
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectAgricultural zones
dc.subjectBayesian network classifiers
dc.subjectClassification rules
dc.subjectCrop fields
dc.subjectFuzzy classification systems
dc.subjectFuzzy rule set
dc.subjectKriging
dc.subjectRisk predictions
dc.subjectWeed infestation
dc.subjectWeed seed
dc.subjectYield loss
dc.subjectBayesian networks
dc.subjectCompetition
dc.subjectInference engines
dc.subjectRisk perception
dc.titleRisk prediction for weed infestation using classification rules
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución