01618naa a2200313 a 450000100080000000500110000800800410001902400370006010000140009724501240011126000090023552006460024465000160089065000190090665000330092565000160095865000220097465000230099665300240101965300160104365300180105965300220107765300270109965300250112670000190115170000200117070000200119077300940121020829862020-01-07       2017    bl uuuu     u00u1 u    #d7 a10.1504/IJMSO.2017.100086382DOI1 aLASSO, E.  aExpert system for coffee rust detection based on supervised learning and graph pattern matching.h[electronic resource]  c2017  aAbstract: Diseases in agricultural production systems represent one of the main reasons of losses and poor-quality products. For coffee production, experts in this area suggest that weather conditions and crop physical properties are the main variables that determine the development of coffee rust. This paper proposes an extraction of rules to detect coffee rust from induction of decision trees and expert knowledge. In order to obtain a model with greater expressiveness and interpretability, a graph-based representation is proposed. Finally, the extracted rules are evaluated using an expert system supported on graph pattern matching.  aAgriculture  aExpert systems  aPlant diseases and disorders  aAgricultura  aDoença de planta  aHemileia vastatrix  aÁrvore de decisão  aCoffee rust  aDecision tree  aFerrugem cafeeira  aGraph pattern matching  aSistema especialista1 aTHAMADA, T. T.1 aMEIRA, C. A. A.1 aCORRALES, J. C.  tInternational Journal of Metadata, Semantics and Ontologiesgv. 12, n. 1, p. 19-27, 2017.