big data

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MAGESTAN project aims to develop new decision support tools for tomatoes greenhouse to optimize production cycles. Development of these tools will build on the new capabilities offered in modeling the high-performance computing and big data.
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Smart Agriculture System project aims to design an original system modeling, simulation, prediction performance and aid to decision agricultural stakeholders: farmers, advisors, seed companies, processors. The final goal is to lead to improved efficiency of inputs for a given performance target (quantity and quality) through optimized modulation inputs (corn crop). The objective of this project is the development of a new decision system to conduct wheat production.
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IrriSmart is a cloud service that implements FAO-56 guidelines for computing crop water requirements from meteorological data and crop coefficients, a key foundation component in decision support systems for irrigation and fertirrigation practices. FAO-56 guidelines provide a standardized guidance to project managers, consultants, irrigation engineers, hydrologists, agronomists and meteorologists for computing crop water requirements for both irrigated and rainfed agriculture and water consumption by agricultural and natural vegetation, allowing a model-based approach in critical business pro
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Tomato plays an important role in European agriculture, both from a social and economic view. In the last years, tomato crop is facing hard challenges due to problems in productivity, competitiveness and profitability. Furthermore, climate changes are jeopardizing EU agricultural products. ICT tools could play an important role in this sector. High quality is a must in horticultural products in addition water shortage increases.
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This project will build a demonstrator to support crop yield improvement by integrating and exploiting plant phenotyping and ‘omics data. This is a strategically important area of unmet need both in the UK and globally, where optimising the combination of inputs, treatments, doses & timings to apply to specific crop strains in a specific growing environment is a massively complex challenge.
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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.
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This proposal aims to develop an automated system for precise application of nitrogen (N) fertiliser and plant growth regulators (PGRs). Algorithms and software will be developed for integrating diverse forms of data from crop sensing instruments, yield maps, soil maps and soil N measurements. This will enable more accurate N fertiliser and PGR management through real-time decision making in the field, both on a field-by-field basis and on a metre-by-metre basis.
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Abstract The project brings together agronomy research, on rapid protein assays for milling wheat, with engineering of photonic sensors, image recognition & mechatronic systems. The ultimate goal is to deliver a tractor-mount scanning unit for autonomous mapping of protein content across wheat fields, to a spatial resolution better than 2 square metres at full field application speeds (17km/hr) for precision application of nitrogen (N). N is the primary input cost and 80% of the carbon footprint, in milling wheat production, however it is over applied in 3 out of 4 cases.
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This project unusually involves a consortium of prime producers and technology suppliers. It is farmers who will actually deliver on sustainable intensification and that is why this project involves them directly as full partners. Entitled Real-time Information Systems for Precision Pig Production, the project will commercially pilot a recently developed system - Guardian Action - as a precursor to full UK industry roll out.
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Consortium members have worked in real-time monitoring and data capture for 15 years. It has shown that significant productivity gains can be made through monitoring.
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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.
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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.
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Abstract The TSB project is a business led collaborative project involving an SME, Rail Vision Europe Ltd (RVL), a not for profit RTO, Rothamsted Research (RR) & a large crop production company, Certis UK Ltd (CUK) a UK subsidiary of Certis Europe BV. The project will be led by RVL. The project brings together a consortium comprising highly established crop specialists from RR and CUK, and electronic sensor, photonics, data processing and analytics systems specialism from RVL.
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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.
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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).

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