Neural network estimation of leaf wetness duration: application to a Plasmopara viticola infection forecasting (Articolo in rivista)

Type
Label
  • Neural network estimation of leaf wetness duration: application to a Plasmopara viticola infection forecasting (Articolo in rivista) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Alternative label
  • Dalla Marta A., M. De Vincenzi, S. Dietrich, S. Orlandini (2005)
    Neural network estimation of leaf wetness duration: application to a Plasmopara viticola infection forecasting
    in Physics and chemistry of the earth (2002)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Dalla Marta A., M. De Vincenzi, S. Dietrich, S. Orlandini (literal)
Pagina inizio
  • 91 (literal)
Pagina fine
  • 96,00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 30 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Titolo
  • Neural network estimation of leaf wetness duration: application to a Plasmopara viticola infection forecasting (literal)
Abstract
  • Leaf wetness duration (LWD) is one of the most important variables responsible for the outbreak of plant diseases but, in spite of its importance, the technology for measurement is not rather reliable. For this reason the modelling appears to be a valid support for LWD assessment. In this work a technique for LWD estimation that was applied in some agro-environmental studies from few years was used: artificial neural network (ANN). The ANN output then was used as input for an epidemiological model to predict Plasmopara viticola infections. The aim of this work was to carry out an ANN capable to find out the relationships between the agrometeorological input and LWD and to evaluate the impact of this estimated LWD when integrated in epidemiological simulations. (literal)
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