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

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Neural network for the estimation of leaf wetness duration: application to a Plasmopara viticola infections forecasting (Articolo in rivista) (literal)

Anno

• 2005-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1016/j.pce.2004.08.016 (literal)

Alternative label

• DALLA MARTA A., DE VINCENZI M., DIETRICH S., ORLANDINI S. (2005)
  Neural network for the estimation of leaf wetness duration: application to a Plasmopara viticola infections forecasting
  in Physics and chemistry of the earth. Parts A/B/C (Online)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• DALLA MARTA A., DE VINCENZI M., DIETRICH S., ORLANDINI S. (literal)

Pagina inizio

• 91 (literal)

Pagina fine

• 96 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.sciencedirect.com/science/article/pii/S1474706504002001 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 30 (literal)

Rivista

• Physics and chemistry of the earth. Parts A/B/C (Rivista)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Department of Agronomy and Land Management, University of Florence, Institute of Biometeorology, National Research Council - Agroecosystem Monitoring Laboratory, Sassari, Italy Institute of Atmospheric Sciences and Climate, National Research Council, Roma, Italy Department of Agronomy and Land Management, University of Florence, (literal)

Titolo

• Neural network for the estimation of leaf wetness duration: application to a Plasmopara viticola infections 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)

Prodotto di

• Sviluppo di competenze (AG.P04.033.001) (Modulo)
• Institute for biometeorology (IBIMET) (Istituto)

Autore CNR

• MATTEO DE VINCENZI (Persona)
• STEFANO DIETRICH (Persona)

Insieme di parole chiave

• Parole chiave di "Neural network for the estimation of leaf wetness duration: application to a Plasmopara viticola infections forecasting" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute for biometeorology (IBIMET) (Istituto)
• Sviluppo di competenze (AG.P04.033.001) (Modulo)

Autore CNR di

• MATTEO DE VINCENZI (Persona)
• STEFANO DIETRICH (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Physics and chemistry of the earth. Parts A/B/C (Rivista)

Insieme di parole chiave di

• Parole chiave di "Neural network for the estimation of leaf wetness duration: application to a Plasmopara viticola infections forecasting" (Insieme di parole chiave)