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D3.1 GRAPEVINE Plagues and Diseases Models v1

Authors: Moshou, Dimitrios; Paraskevas, Charalampos; Pantazi, Xanthoula-Eirini; Kechagias, Konstantinos; Tamouridou, Afroditi-Alexandra; Pliatsidis, Konstantnos; Pantazi, Evdoxi-Glykeria; +4 Authors

D3.1 GRAPEVINE Plagues and Diseases Models v1

Abstract

This document is issued within the frame and for the purpose of the GRAPEVINE project. This project has received funding from the European Union's Innovation and Networks Executive Agency – Connecting Europe Facility (CEF) under Grant AGREEMENT No INEA/CEF/ICT/A2018/1837816; Action No 2018-EU-IA-0091. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Commission. This document and its content are the property of the GRAPEVINE Consortium. All rights relevant to this document are determined by the applicable laws. Access to this document does not grant any right or license on the document or its contents. This document or its contents are not to be used or treated in any manner inconsistent with the rights or interests of the GRAPEVINE Consortium or the Partners detriment and are not to be disclosed externally without prior written consent from the GRAPEVINE Partners. Each GRAPEVINE Partner may use this document in conformity with the GRAPEVINE Consortium Grant Agreement provisions

The main objective of Activity 3 (Models specification and implementation) of GRAPEVINE project is to define several models for the identification of diseases and plagues, and the way to mitigate their effects. These models represent: a physical model about the behaviour and evolution of diseases / plagues, identification of the diseases / plagues in crops, accurate weather forecasting, crops phenology and a deep learning model for disease / plague classification and decision making. This deliverable (in the first version v1) focuses on defining the basic models (phenological, disease and meteorological) that will be used in the implementation of the other tasks of Activity 3. These models will be combined in one global model in order to provide predictions through coupled simulations / calculations.

Keywords

Disease model, Datasets, Phenology model, Meteorology model

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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