In this project we will develop an evaluation platform that demonstrates through research the potential for an Internet of Things (IoT) enabled FMIS with animal-centric ICT, production databases & best practice standards to assist farmers optimise sustainable livestock production. In this respect SILF will take an integrated approach to solving issues with environmental impact and animal welfare during livestock production.

The target of 3D-Mosaic is to promote precision management of orchards by means of a decision support system (DSS) aiming to optimize efficiency of inputs including water and to diminish the environmental footprint of fruit production. The DSS will apply information and communication technologies (ICT) for precision management of the most economically relevant tree crops, apple and citrus. For this purpose, sensors, monitoring strategies, information processing and decision support systems will be developed. Together, these will produce maps for orchard management including irrigation.

The PredICTor project has two main deliverables, i) an online decision support tool for evaluating an intended field traffic situation for a given soil condition and with given machinery (for farmers and agricultural advisers), and ii) an online tool for creating European-wide maps of the wheel load carrying capacity, which is defined as the maximum wheel load the soil can carry at given soil moisture conditions and for a given tyre and tyre inflation pressure (for authorities / soil protection offices).

The aim of the project is to develop an ICT based tool for performance and welfare monitoring of pigs at the individual level. Warning signs, such as alterations in animal behaviour and some other parameters, enable an early detection of diseases or environmental related problems. Since the routinely gathering behavioural information from animals to evaluate their performance and welfare is very time-consuming for farmers, the new technologies demonstrably aid this task (Wathes et al., 2008), especially with large herds.

Soil-for-Life® (SfL) drives continuous improvements in crop production/utilisation resulting in direct increases in marketable yield and operational efficiencies. SfL is underpinned by an emerging innovative interdisciplinary field ‘agri-informatics’ whereby statistics and database management techniques are used to exploit knowledge held in multiple ’big data’ sets.

This project seeks to develop innovative 3D imaging technology to enhance the simultaneous measurement of cow body condition score (BCS), liveweight and mobility (gait) as a highly advanced management decision-making tool. The aim is to improve the pace at which these key quality and production traits are identified for animal welfare, sustainability and profitability.

Abstract Foodborne disease is highlighted as a top priority for the food and beverage industry, responsible for £1.5Bn pa global economic losses (FSA, 2013). Nearly 70% of foodborne disease arises from Campylobacter, Salmonella and E.coli, which are major threats to the health and safety of the food supply chain (FSA, 2014).

Water availability in Switzerland varies by region, and water scarcity during the summer is expected to increase due to climate change. Thus, water consumption for irrigation will increase in order to ensure food security. In today’s irrigation systems, the amounts of water used often exceeds the amount of water required by plants.

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) . The aim of CropWatch is to develop a functional demonstrator of an information system for monitoring and analysis of plant production process. It basically consists of a server-based data management system with integrated facilities for receiving, storing, processing and analysis of data of different origins from the plant production process.

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). Collaborative project: Decision Support in Crop Protection: Pest Identification using UAV Technology The german project description will be translated soon. Im Rahmen des Schadinspektor-Projektes werden Algorithmen zur Identifikation und Abgrenzung von Schaderreger-Befallszonen innerhalb landwirtschaftlicher Schläge entwickelt und durch die Integration als Webservice in das Internetportal von www.isip.de der landwi

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). Remote sensing of weed infestations For an improved weed control which is adapted to the current situation in the field, the farmer needs information about the weed infestation to specifically adjust the management of weed control. In the project REMWEED tools are developed to determine the weed infestation spatially.

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). Control and optimization of harvest and storage for apple with data-based prognosis models for improvement of fruit-quality and reduction of storage-losses In Germany, fruit quality losses and rots occurring from harvest to consumption are estimated to be up to 18%. In addition to the direct loss of food for human consumption there are also considerable losses of natural resources and human labour.

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). development of methods for pre-symptomatic and specific detection of grape diseases like esca, phytoplamosis and viruses as basis for a regional monitoring in vineyards and development of control strategies# Ziel des Vorhabens ist die Entwicklung von Verfahren zur Früherkennung/Frühdiagnose von endogenen Problemkrankheiten der Weinrebe wie Esca, Phytoplasmosen und Virosen.

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). Sensor-based online-detection of pests in wheat Aim of the project is the development of a suitable technology for the early detection of yellow rust patches in wheat. Optical vehicle- and UAV-carried sensors will be tested. For a disease related control decision by the farmer beside the information of the disease also information of various plant parameters like crop surface and plant height are necessary.

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) . Resource efficient plant protection based on a data driven multi-scale approach for the process chain: Diseases detection - decision support - demand specific fungicide application In precision agriculture most current solution focus on site specific fertilization.