Neural network for the estimation of leaf wetness duration: Application to a Plasmopara viticola infection forecasting
Dalla Marta, A. , De Vincenzi, M., Dietrich, S., Orlandini, S. Neural network for the estimation of leaf wetness duration: Application to a Plasmopara viticola infection forecasting. Physics and Chemistry of the Earth Volume 30, Issue 1-3 SPEC. ISS., 2005, Pages 91-96
Dalla Marta, A. , De Vincenzi, M., Dietrich, S., Orlandini, S.
Neural network for the estimation of leaf wetness duration:
Application to a Plasmopara viticola infection forecasting. Physics
and Chemistry of the Earth Volume 30, Issue 1-3 SPEC. ISS., 2005,
Pages 91-96
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.
