The ability to optimize inputs at spatial scale has been enabled with the use of Variable Rate Applications (VRA). Despite the fact that there are numerous VRA systems aimed at arable crops, specific systems for orchard management utilizing precision monitoring and application of the orchards are still lacking. These cropping systems face increasing market pressure to produce quality products, and provide a detailed traceable system for the origin of the product including, the treatments and the conditions that have occurred during the production.

This project aims to contribute to the penetration of agricultural robots into commercial markets by further developing a teleoperated robot, which was implemented in a previous project (AgriRobot). The main idea of this project is to combine the two existing directions (fully automated and teleoperated robot) in order to design, develop, test and evaluate a Semi-Autonomous Vineyard Spraying Agricultural Robot (SAVSAR).

In response to potential loss of herbicides due to EU Directives and regulations and other environmental pressures, the project is carrying out basic and applied research to facilitate the adoption of targeted patch spraying of grass weeds and selected broad leaved weeds in arable fields in the UK. Machinery and systems are already available for patch spraying. The barrier to adoption is knowing where the patches are located with the precision required.

Abstract Commercial production of wheat crops in the UK is currently highly dependent on timely applications of fungicides to optimise yield and the development of improved varieties by plant breeders with resilience to diseases and abiotic stresses. The bottleneck is now in the ability to conduct field-based discovery and evaluation of traits (phenotyping) which are currently laborious, time consuming and inefficient.

Abstract Potato late blight is one of the world's most devastating crop diseases, responsible for £3.5Bn pa global economic losses (AHDB, 2011). BlightSense will incorporate low-cost, antibody-coated sensing consumables with a proven (Rotarod) air-sampling spore trap, with a view to producing a fully integrated wireless product to be placed at various locations in the field to help map the blight risk.

Abstract The instrument will form the basis of a distributed detection network, providing real-time information on inoculum movement, allowing more effective timing and targeting of fungicide control. The work involves integration of cyclone air sampling, automated fluidic handling and DNA analysis using Loop mediated isothermal AMPlification (LAMP) methods for direct detection and identification of fungal species.

The project will develop an autonomous robot allowing >50 % herbicide reduction in sugar beet weeding. New weed detection vision algorithms will be developed and ported to a graphics processing unit. A low-power robotic arm and robust multi-sensor navigation will be designed. Five solar-powered prototypes will be validated in real operation. The robot will significantly reduce weeding cost, allowing return on investment in 3 years. The project is aimed at distributing the robot in all of Europe, with envisaged sales of 1000 robots per year from 2018 on.

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.

Development of an extreme weather monotoring to assess the risks and for provision of decision support in extreme weather management for agriculture The project aims to develop and implement an extreme weather monitoring und risk assessment system (EMRA) which quantifies the acute, chronic and future risk potential of extreme weather events for agricultural production as well as to provide practical decision support tools for the agricultural extreme weather management.

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.