Research objectives Use hyperspectral data from UAV platform for crop/weed discrimination Generation of spraying task /application maps for SSWM and validation The research will focus on two kind of weeds i.e. thistles in grassland and bindweeds in maize fields for case study. Field aerial hyperspectral images of thistles will be collected at the different growth stages and different flight height. The aerial images of bindweeds will also be taken in the early growth stage in the maize fields.

This project uses hyperspectral camera's and multi-exposure to predict the landing of individual fertilizer granules.

The present production and delivery of real-time crop protection information is not optimal and there are bottlenecks to work on. This project will renew the way this valuable information is produced, shared and delivered. With an easy-to-use app, we shall activate farmers and forest owners to monitor crop pests. Together with helping to carry out monitoring and making and sharing pest observations the app service will be capable of delivering up-to-date expert information on crop pests and pathogens. The project participants share and build commitment to produce this expert information.

Information about the weed population in fields is important for determining the optimal herbicides for the fields. A system based on images is presented that can provide support in determining the species and density of the weeds. Firstly, plants are segmented from the soil. Plants that after the segmentation are divided in multiple parts are selected manually and a cost image is created by weighting pixels according to their relationship to plant. This relationship is based on the colours of pixels and the weighting of nearby pixels.

Development of vision technologi for detection of weeds in early development stage. Implementation in a weeding robot with thermal destruction of weeds. Suitable for production of organic food.

Study and carring out of a smart sensors system based on multivariate models to limit unfavourable events in viticulture. Aim of the project is the development of a system for plant protection products uses in viticulture, which is guided in real time via internet and based on a previsional model.

The main objective of the project is the optimization of plant protection products as crop protection mechanism through the use of new design techniques and electronic control technologies integration which achieve a higher efficiency of treatments, maintain the crop quality, reduce costs and minimize the plant protection impact on environment.

The project is based on using mobile devices to track the expense of products during the application of these ones with an agricultural tractor. This is achieved by recording params like gps positions and velocity of tractor. What products to apply and their dose are initially load in the mobile device from a server where the treatment recommendations have been generated previously with a web application or from the GIS Application.The obtained results in the mobile device are sent to a server using Sigfox Technology and are shown in a gis web application.

Precision agriculture for the sustainable management of weed. Aim of the project is to design and assess the effectiveness of a management system for synthetic herbycides using precision agriculture technologies. By means of recent innovations in electronics for machine control, remote sensing, and GPS what is to be proofed is the possibility to remarkably reduce the quantity of herbicides and increasing the treatment efficiency.

In agriculture sector, an incorrect application of phytosanitary products increases the production costs and the environment impact on public health and on natural resources, causing soil and water contamination and decreasing the productivity. Furthermore, an excess application can extinguish other species and can affect to workers, farmers and close population, in addition to negative effects on environment. Current machinery can reach 4 km per hour with a width of 14 meters and applying products on 6,6 hectares per hour.

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.

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 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.

To fight the development of golden flavescence the DAMAV project aims to develop an automated testing solution vine diseases rollover parcels via a micro-drone. The objective is to provide a turnkey tool for winemakers to allow the search for potential outbreaks, and more generally, any type of disease detectable vine foliage. To enable this diagnosis, the partners propose to study the foliage with a drone and a multispectral high-resolution camera.

Broad-leaved dock (Rumex obtusifolius L.) is a common and troublesome weed with a wide geographic distribution. The weed is readily consumed by livestock but its nutritive value is less than that of grass. The high contents of oxalic acid and oxalates can affect animal health if consumed in larger doses. When left uncontrolled, the weed will reach a high density and reduce grass yield by 10 to 40%. In conventional dairy farming, the weed is normally controlled by using herbicides. In organic farming no synthetic pesticides are used and there is a risk that broad-leaved dock will spread.