This project is funded by the innovation program of the Federal Ministry of Food and Agriculture (BMEL), funding agency is the Federal Office for Agriculture and Food (BLE) . This project aims at the development of a web-based interactive planning system as a comprehensive tool for crop management in cereals. The user is to be conducted by an intuitive decision support for all processes throughout the season from sowing to harvest.

This project was funded by the innovation program of the Federal Ministry of Food and Agriculture (BMEL), funding agency was the Federal Office for Agriculture and Food (BLE) . Today, planning and application of agricultural pesticides, observancy of field boundary structures and regulations around water courses, including documentation are within the competence and control of the driver who carries out the agricultural process.

Objectives The principal objective of this project is to design and develop mobile and web-based applications in order to enhance crop variety selection activities (real-time data accessibility, data analytics, and decision making). More specific objectives are to: 1.Identify and examine various crop management and performance data sources and data format. 2.Collect data in a centralized format (i.e.

Producing the highest quality crop with a maximized yield requires the real-time measurement and actionable analysis of a large number of plant-related external factors. These factors are meteorological, geological, organic and human-related. Measuring and processing these parameters is at the core of any decision-support system intending to streamline the production of farmlands. Currently there are no solutions available that can quantify all on-site plant-related parameters in real-time.

The objective of this project is to propose to wine growers and to oenologists an integrated approach from vineyard to winery, to enhance nitrogen management according to the wine aimed profile. Indeed, the nitrogen content of the grape must is an essential criterion to control and regulate the aromatic quality of the obtained wine and so for their export competitiveness.

MAGESTAN project aims to develop new decision support tools for tomatoes greenhouse to optimize production cycles. Development of these tools will build on the new capabilities offered in modeling the high-performance computing and big data.

Smart Agriculture System project aims to design an original system modeling, simulation, prediction performance and aid to decision agricultural stakeholders: farmers, advisors, seed companies, processors. The final goal is to lead to improved efficiency of inputs for a given performance target (quantity and quality) through optimized modulation inputs (corn crop). The objective of this project is the development of a new decision system to conduct wheat production.

The project involves the development of new crop suited in nurseries and vineyards to significantly reduce the impact of diseases. The proposed ADVANTAGE protection strategy is based on combinations of protection products or techniques and also on decision-support tools. These may be based on image processing techniques and/or molecular modeling tools.

The project is part of the strategic and economic context of precision farming. The objective is to maximize farmers revenue by minimizing the environmental impact of agricultural practices. One of the idea is to modulate practices in intra-plot level. The main technical obejctive is to develop a long-range drone imaging system and the image processing algorithms and associated agronomic models to supply the tools for decision system needed to implement precision agriculture crop Wheat, Maize, Sunflower and Rapesee.

IrriSmart is a cloud service that implements FAO-56 guidelines for computing crop water requirements from meteorological data and crop coefficients, a key foundation component in decision support systems for irrigation and fertirrigation practices. FAO-56 guidelines provide a standardized guidance to project managers, consultants, irrigation engineers, hydrologists, agronomists and meteorologists for computing crop water requirements for both irrigated and rainfed agriculture and water consumption by agricultural and natural vegetation, allowing a model-based approach in critical business pro

Tomato plays an important role in European agriculture, both from a social and economic view. In the last years, tomato crop is facing hard challenges due to problems in productivity, competitiveness and profitability. Furthermore, climate changes are jeopardizing EU agricultural products. ICT tools could play an important role in this sector. High quality is a must in horticultural products in addition water shortage increases.

In the IMPRESS project partners will realize two (synergistic) products: 1) the FI-sense platform, an open sensor platform that can be used by any sensor producer to receive and transmit data of wireless sensors in greenhouses to FIspace. The platform uses frequencies and protocols that are suitable for greenhouse conditions. 2) The AquaTagRemote soil moisture sensor. The low cost wireless soil moisture sensor will make it possible to control the irrigation based on the real soil moisture conditions.

The focus of this project is to develop and enable an intelligent system that will apply precision management to whole farm grassland and grazing systems. The goal is to optimize grass quality, utilization efficiency, and ultimately profitability, with minimal labour requirement and maximum objectivity. To precisely allocate to the cow herd the absolutely correct area of grass, it is necessary to have an accurate ‘real-time’ measure of grass quality (as well as quantity).

Profitability on grass-based systems is driven by degree of grass utilization. This is influenced by increased growth and optimum management of that growth. Frequent measurement of grass parameters, e.g. herbage yield, height, density will facilitate increased herbage production and utilization. However, traditionally such measurement on farms is limited. The potential use of ICT for grass measurement is dramatic.

Ignoring the inherited spatial variation in soil properties with traditional sampling methods leads to poor crop management, yield loss and excess use of input. The proposed system of FarmFuse addresses these issues in 2 ways: (a) utilising a new and innovative on-line multi-sensor platform for measuring key soil properties at an appropriate resolution. (b) Integrating this improved soil data with other information such as vehicle-borne sensing of crop growth, weather data, soil conductivity and yield maps, to develop algorithms to determine rules for variable rate application.