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.

Lowland areas and other marginal areas form a huge and currently unused resource of biomass for the biogas and bio refinery industry. Alone for Denmark, it has been estimated that 160-180.000 hectares of unused lowland could be harvested if the appropriate technology was available. This project will develop a novel lightweight, autonomous machine concept for economically and environmentally sound harvest of grass on lowland.

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).

The project ‘Multi channel Disposable Sensors for Animal Health Disease Diagnostics’ aims to develop microfluidic, electronic biosensors for Bovine Respiratory Disease (BRD). BRD is a leading natural cause of death in cattle and has substantial economic impact on the US and Irish food industries. BRD is typically diagnosed via ELISA, which can be expensive and slow to provide definitive results. There are at present no commercially-available field-based electronic tests for animal diseases.

Abstract This project aims to design, develop and implement an innovative web-based decision support platform (soilquality.org.uk) for improved soil management in UK arable and lowland grassland systems. Using levy funding, farmers' demand for tools to manage soil health has been met in Australia through soilquality.org.au. The soilquality.org.au platform, which provides the ability to compare individual test results within a robust decision-support framework, is unique and highly valued by farmers' organisations.

The main objectives of this project are (1) to develop a working prototype of an artificial fruit sensor system that mimics the size, shape and composition of fresh fruit, and thereby also its thermal behaviour, and that logs core and surface temperatures, (2) develop a manufacturing method for this artificial fruit, (3) prove the feasibility of the prototypes for monitoring fruit temperature history in cold chain operations by lab and field experiments.

Abstract In current practice, a tractor mounted sensor to calculate Normalized Difference Vegetation Index (NDVI) detects live, green vegetation from a target area and can be used to analyse crop nutritional requirements. By adding high-resolution satellite data it is possible to achieve a variable rate (VR) fertiliser recommendation. Current practice lacks two key factors in the determination of optimum N supply to growing crops: availability of high-resolution data to inform on soil fertility status; and technologies that ensure accurate and consistent placement of nutrient.

Abstract This research proposal specifically targets Campylobacter infection of broiler chickens to demonstrate the utility of this sensor-based detection system in addressing a significant global meat production problem that impacts on animal health and welfare, and human health. Campylobacter jejuni causes >460,000 reported campylobacteriosis cases annually, at a cost to the UK government of >£900 million.

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 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).

Abstract AUTOPIC is a multi disciplinary project aimed at mechanising the harvesting of soft fruit through the use of autonomous vehicles and robotics. Partners include Harper Adams University, the Shadow Robot Company, Interface Devices Limited and the National Physical Laboratory. The project is timely since the source of migrant seasonal fruit pickers is no longer supported by the Seasonal Agricultural Workers Scheme and in general migration is being discouraged by government policy.

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.

Using precision technologies, technology platforms and computational biology to increase the economic and environmental sustainability of pasture based production systems. PRECISIONDAIRY will develop a prototype platform that combines the disciplines of sensor/biosensor development, communication standards and database design with modellers, existing databases and mathematicians with the objective of increasing the environmental and economic sustainability of Irish pasture based dairy farms.