title: A two years study results in the use of artificial neural networks to forecast Plasmopara viticola infection in viticulture.
abstract: This paper describes the further results of the study that has been described in session 5 of the 58th International Symposium on Crop Protection (Ghent 2006). Since then our attention has been focused on verifying the previous communication results working on a two years basis data set belonging to a specific farm. The choice of using data from a single farm derives from the considerations that have been explained in the previous study in which it was clear that an efficient forecasting Artificial Neural Network (ANN) model can be created only in restricted (or at least comparable) pedoclimatic areas. On the basis of the matured experience, at the moment we have realized an ANN which, being trained on 2005 year data, elaborating the following year data is capable of correctly predicting the real Plasmopara viticola (Berk. et Curt.) Berl. et De Toni outbreak, never giving false negative signals (no alarm in presence of infection on the field) and, finally, giving few other alarms which are totally comparable with the ones given by the most common statistical instrument used in this field trials. We confirm the advantages of this approach in terms of: (a) Management and optimization improvement of agricultural activities. (b) Reduction of plant protection products use. (c) Quality improvement of the final product for a real lowering of plant protection products use. (d) Reduction of environmental impact. (e) A more efficient management of the climate changes.
authors.affiliation: Via Montagnano 34, IT-00040 Ardea, Roma, Italy. r.bugliosi@deca-srl.com
authors.forename: R
authors.initials: R
authors.lastname: Bugliosi
authors.forename: G
authors.initials: G
authors.lastname: Spera
authors.forename: A
authors.initials: A
authors.lastname: La Torre
authors.forename: L
authors.initials: L
authors.lastname: Campoli
authors.forename: M
authors.initials: M
authors.lastname: Gianferro
authors.forename: S
authors.initials: S
authors.lastname: Talocci
mesh_terms: D000383:Agriculture / Q000379:methods*; D002980:Climate; D005544:Forecasting; D008954:Models, Biological; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009868:Oomycetes / Q000254:growth & development*; D010571:Pest Control / Q000379:methods; D013997:Time Factors; D027843:Vitis / Q000382:microbiology*