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).

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.

The aim of this 3 year project is to develop the potential for using crop sensing to more precisely target input applications such as fertilizer, plant growth regulation and crop protection, and then to structure this knowledge as a support tool to allow advisors and growers to confidently use this technology to improve efficiency, sustainability and competitiveness on commercial farms. One of the critical challenges for the successful and widespread adoption of Precision Farming in Europe is to mainstream the use of the technologies to ensure it is accessible to all farmers and can become

The main goal is to optimise the feeding strategy for grazing cattle and to improve the methods of pasture management. An in the frame of the project developed pasture robot and a modified automatic grazing system (AGS) will be integrated into existing herd management software (HMS), providing an optimal feeding strategy for cattle and pasture maintenance. A robot will be redesigned and sensors for detection of biomass, cowpats etc. and actuators, a mulcher and a seeder, will be implemented.

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.

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.

This project proposes implementation of precision agriculture technologies and methods in the beekeeping. Precision agriculture approach is adapted for beekeeping based on the various measurements of individual bee colonies all year around thus detecting different states of colonies and apiaries enabling rapid reaction by the beekeeper in case of necessity. Digital measurements such as temperature, humidity, audio and video can be used to detect several states of a bee colony: swarming, broodless stage, brood rearing, illness.

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 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.

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.

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.

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.

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 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.