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.
Neural network estimation of leaf wetness duration: application to a Plasmopara viticola infection forecasting
M De Vincenzi;
2005
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
