This project focuses on the optimization of viticultural plant protection through a new technological product, based on intelligent processing of data and prediction models to optimize pest control. Spraying decision in plant protection is a key issue concerning agrochemicals use and crop expenses, which are a substantial factor in viticulture. Technological updating and the development of environmental-friendly technologies safe for consumers are required to ensure agriculture competitiveness in Europe, according to the Report on the Competitiveness of the European Agro-Food Industry (HLG007/2009). Sustainability of the agro-food sector depends on the reduction of its weaknesses through fostering new technologies. Currently, many pest management decisions in viticulture are based on the consideration of relevant data sources, combined with farmer’s experience and intuition. These decisions are often improved through the use of disease forecast models, based on meteorological data, infestation observations in the field, and crop status updates. The development of a forecast model is usually based on existing experience, structured gathering of field and meteorological data, field experiments and verifications. The model which is built during this time consuming process is specific to the conditions in which it was developed, and is less accurate under different general and micro climatic regions, growing protocols, varieties, topography etc. The innovation of the project is the development of a platform that will allow substantial improvement in the adaptation and implementation process of existing models to various vineyards according to their local conditions and properties, for better decision making. Thus, it will contribute to reduce crop protection agents and other agrochemicals use, and increase the positive impact on the environmental pressure of viticultural practices throughout Europe. Furthermore, optimized use of agrochemicals and water will lead to an increased agricultural yield. Technically, the project aims at developing an integrated data analysis and visualization system for viticultural plant protection decision support. The system should enable effective and user friendly implementation of forecast models, and support their continuous localized adaptation down to the plot level. A Web based user friendly multilingual Software as a Service (SaaS) product is aimed, which will combine, analyze and display meteorological data down to sub-plot microclimate and field infestation data collected by pest scouts, in an intelligent, flexible and immediate decision support system. Algorithms for data analysis will be developed for enabling the system to automatically adjust prediction models to specific conditions of users, e.g. local topography, micro-climate, effect of treatments etc., including continuous verification of predictions in order to improve localized adaptations. The developed system will be suitable for various users in the viticultural sector – growers of all sizes, wineries, cooperatives, exporters, extension services, researchers and public organizations, thereby providing an affordable choice for spraying decision support in the vast viticultural market. Although the developed system will focus on viticulture, a generic methodology will be used in order to allow easy and fast future customization for various other crops and pests. The project consortium will be based on technological and scientific/agronomic partners: 1. QuantisLabs Ltd. (Hungary) Specialized in data management and specialized wireless sensor networks for viticulture with real time intelligent systems, and with wide experience in the development of sensors and meteorological data networks (QuantisLab’s SmartVineyard(TM) sensor station project has been selected among the top 15 project by the Stanford University Graduate School of Business in 2011). 2. ScanTask (Israel) Specialized in development of software & hardware systems for data management of mobile workers. Its PestScout system is already used for gathering, analysis and presenting pest scouts' field observations. 3. BME (Budapest University of Technology and Economics) specialized in artificial intelligence, data management and development of generic algorithms in the framework of this project. Sub-contractors (for defining market requirements and for field experiments): 1. Migal (Israel) Agricultural R&D center, managing the agricultural research in the Israeli Galilee region. 2. Carmel wines (Israel) Largest Israeli winery, with vast experience in viticultural management and plant protection. 3. Hardware production and assembling (Hungary)