Digital Innovation Hubs Digital Innovation Hubs

AgriTech Big Data AgriTech BigData, Plataforma de innovación digital al servicio del sector agroalimentario ( Agri Tech BigData)

Contact Data

Coordinator

Eurecat (EUT)

Year Established

2017

Location

Parc de Gardeny s/nº, Edifici H3, Planta baixa, 25003, Lleida (Spain)

Website

http://www.agritech-bigdata.com/

Social Media

Contact information

Gabriel Anzaldi Varas
gabriel.anzaldi@eurecat.org
+34 97 319 36 60

Description

Description

Big Data Innovation Hub at the service of the agri-food sector (Agri Tech BigData)

AgriTech Big Data is an initiative led by Eurecat and The University of Lleida that will incorporate other promoters as they add value and align with Strategic Objectives. The main strategic objectives are: 1) Create a space of innovation that helps to promote and make effective the value of data for companies in the agri-food industry: AgriTech Big Data will identify valuable use cases and will demonstrate its benefit with practical results, which will be achieved through the execution of innovation projects. 2) Integration of the key technological offer in Big Data for the agri-food and environmental sectors: AgriTech Big Data will be able to implement Big Data innovation projects. Therefore, the initiative is born to become an integration agent able to solve the challenges related to the interoperability of digital systems, the exchange of standardized information, the creation of data repositories, the treatment and analysis of data, and the development of advanced visualizations tools. 3) Train companies and professionals in the field of Big Data: One of the main commitments of AgriTech Big Data will be to transfer the knowledge, obtained from its activity, and to promote training to develop professional skills. This commitment will be accomplished through different activities that will include; knowledge transfer in innovation projects, the provision of specialized training services, and collaboration with other organizations and training entities. 4) To become an international reference for the agri-food sector: In order to achieve this goal, best practices in specialized media and social networks will be disseminated, presence in relevant events will be managed, and alliances with other international agents experts in Big Data will be planned. These strategic objectives are complemented by the following specific objectives: 1) Contribute to the valorisation of the data to develop decision-making and analysis tools. 2) To improve efficiency and productivity of industrial processes by means of an optimal analysis of the data, with emphasis in the analysis of real time. 3) Contribute to the approximation of companies providing technology to the agro-food sector, so that new solutions can be implemented based on the acquisition and analysis of data, with an emphasis on real-time analysis. 4) Maximize the quality, efficiency and application of advanced data analysis techniques in all agricultural business lines, resulting in greater confidence and consistency in decision processes. 5) Contribute to increase the competitiveness and knowledge of the agro-food sector, and the development of new business models to accelerate the implementation of innovative technological solutions in the market. 6) Provide guidance and recommendations to the companies in the definition and implementation of their technological strategies and roadmaps of Big Data solutions, as well as offering services for training of domain professionals (Data scientist, Data engineers and Data Business Analyst).

AgriTech Big Data es una iniciativa liderada por Eurecat y La Universidad de Lleida que incorporará a otros promotores o colaboradores en la medida que aporten valor y se alineen con sus objetivos estratégicos. Los principales objetivos estratégicos del iniciativa son: 1) Crear un espacio de innovación que ayude a promover y hacer efectivo el valor de los datos para las empresas de la industria agroalimentaria: AgriTech Big Data permitirá identificar los casos de uso de valor y demostrará su beneficio con resultados prácticos, que se adquirirán a través de la ejecución de proyectos de innovación. 2) Integración de la oferta tecnológica clave en Big Data para el sector agroalimentario y medioambiental: AgriTech Big Data será capaz de ejecutar proyectos de innovación Big Data. Por tanto, la iniciativa nace con la voluntad de convertirse en un agente de integración y alineamiento tecnológico capaz de resolver los retos relacionados con la interoperabilidad de los sistemas digitales, el intercambio de información estandarizado, la creación de repositorios de datos, el tratamiento y análisis de éstos, y las visualizaciones avanzadas. 3) Capacitar empresas y profesionales en el ámbito Big Data: Uno de los compromisos de AgriTech Big Data será el de transferir el conocimiento que se obtenga de su actividad para impulsar la capacitación, el reciclaje y el desarrollo de profesionales en un ámbito de gran proyección profesional. Este compromiso se materializará a través de diversas actividades que incluirán, la transferencia de conocimiento en los proyectos de innovación, la provisión de servicios de formación especializados, y la colaboración con otras universidades y otras entidades formativas. 4) Convertirse en un referente internacional para el sector agro-alimentario: Para alcanzar este objetivo, se divulgarán las mejores prácticas en medios especializados y redes sociales, se gestionará la presencia en eventos relevantes, y, como pilar principal, se organizará un Simposio AgriTech Big Data de referencia Nacional / estatal. Del mismo modo, se buscarán alianzas con otros agentes internacionales expertos en tecnologías Big Data, para formar parte de las redes de conocimiento mundial más relevantes en el entorno en el tratamiento de datos en el sector. Estos objetivos estratégicos se complementan con los siguientes objetivos específicos: 1) Contribuir a la valorización de los datos que se pueden obtener en el sector agro-alimentario en todos los niveles de manera que se puedan desarrollar herramientas de decisión y análisis que permitan encontrar soluciones a problemas prácticos propios de este ámbito de actividad. 2) Facilitar la eficiencia y la productividad de los procesos productivos mediante un análisis adecuado de los datos, haciendo énfasis en el análisis de tiempo real. 3) Contribuir a la aproximación de las empresas proveedoras de tecnología al sector agro-alimentario de manera que se puedan implementar nuevas soluciones basadas en la adquisición y análisis de datos, haciendo énfasis en el análisis de tiempo real. 4) Maximizar la calidad, eficiencia y aplicación de técnicas avanzadas de análisis de datos en todas las líneas de negocio agrícolas, resultando en una mayor confianza y consistencia en los procesos de decisión. 5) Contribuir a incrementar la competitividad y el conocimiento del sector agro-alimentario y el desarrollo de nuevos modelos de negocio para acelerar la implantación de soluciones tecnológicas innovadoras en el mercado. 6) Asesorar y acompañar a las empresas del sector agro-alimentario en la definición e implantación de sus estrategias y roadmaps tecnológicos de soluciones Big Data, como así también Ofreciendo servicios para la capacitación de los profesionales del dominio (Data scientits, Data engineers y Data Business Analyst) y un programa de divulgación de las tendencias y casos de éxito en Big Data.

Link to national or regional initiatives for digitising industry

Digital transformation has introduced a new reality that changed the practices of organizations for the benefit of competitiveness. Technology has become available to all economic sectors and particularly are helping to face challenges of agriculture, livestock, food production and agri-food business. Innovative solutions are needed through digital applications and technologies for such issues as product demand, market variability, and the price of raw materials or the need to protect the environment (particularly, the water). The technology transfer, technology scouting, training and knowledge about these new technologies that allow to obtain relevant information to make decisions about needs, problems and new challenges, is key to the correct use of the opportunities they offer. Due to the increasing need to make precise and frequent decisions in agri-food matters, the detailed analysis of the information generated or available in the sector must become a key matter in the short and medium term. In this context, it is clear that adequate strategies for collecting and treating large volumes of data must be available and defining methods of analysis that allow faster decision-making in each case. Although concrete applications have begun to develop, massive data analysis techniques, they are still far from the daily operational processes of agri-food production and represent a significant shortfall; as a result, they offer an opportunity that must be taken advantage of.

Market and Services

Market sectors

  • Agriculture, hunting and forestry
  • Fishing
  • Manufacture of food products, beverages and tobacco
  • Manufacture of wood and wood products

TRL Focus

  • TRL4 - Component and/or breadboard validation in laboratory environment
  • TRL5 - Component and/or breadboard validation in relevant environment
  • TRL6 - System/subsystem model or prototype demonstration in a relevant environment
  • TRL7 - System prototype demonstration in an operational environment
  • TRL8 - Actual system completed and qualified through test and demonstration

Services provided

  • Awareness creation
  • Ecosystem building, scouting, brokerage, networking
  • Collaborative Researchs
  • Concept validation and prototyping
  • Testing and validation
  • Pre-competitive series production
  • Commercial infrastructure
  • Incubator/accelerator support
  • Voice of the customer, product consortia
  • Market intelligence
  • Education and skills development

Service Examples

Smart Farm, the service is based on the deployment of an architecture capable of integrating, contextualizing and selecting relevant events on the basis of different sources of information. These relevant events are selected based on user experience combined with patterns recognition under the Big Data paradigms. Finally, to support decision-making, information as well as sources are managed from a dynamic visualization environment and adaptable to the needs of users and the domain. In addition, users are able to detect and / or select events (critical data) to support their decision-making. Finally, this architecture was demonstrated within the agri-food sector. This architecture is deployed in the field of quality management of fruit and vegetable production as well as in the milk production process. Both of the case studies are of importance for the criticality of the processes, where real time decision making is necessary in order  to avoid quality degradation in the raw material.

Customer name, contact details
Product or beef (confidential).
Gabriel.anzaldi@eurecat.org


Internet of Extensive Crops platform main objective is to improve the productivity, efficiency and resilience of extensive farms, by supporting agronomic management through technologies based on the Internet of Things and Big Data. In this sense, the service aims to take advantage of the tools and facilities, giving them greater capacity to collect information and to interact with resources available on the Internet in order to facilitate a more accurate management of all the crop cycle. The main feature consists of contributing to improve decision making when planning the main agronomic operations (preparation of the land, planting, pickling, irrigation, harvesting), with online support during the execution of these operations (sowing and harvesting) and even automation (control of fertigation). It will also support maintenance tasks for machinery and facilities, to make them more economical and sustainable..

What service(s) provided (different from example?
1. Digitization of processes
2. Proof of concept
3. Smart recommenders
4. Platform as a Service
5. Interoperability and integration

The relation with digitization?
Implementation of a platform for processing, analysis and visualization of sensor data in agricultural machinery and infrastructure to support variable management models and predictive maintenance
The service seeks greater coordination between the different operations and actors involved in the extensive crops value chain. From a technical point of view, a key aspect is the interoperability between the data collected or consumed by the various applications used in any of the operations or actors involved in this field.

Name customer, contact details
Empresa de maquinaria agrícola (confidencial).
gabriel.anzaldi@eurecat.org


Simulareg is a set of tools over an accessible web platform to optimize farmer practices acting as a personal irrigation coach. In this way, the platform will improve water irrigation schedules used by farmers to finally get the overall objective of reduce water consumption. It simulates, in a virtual crop, the irrigation schedules proposed by users. At the same time, it compares the results with the irrigation scheduling that an expert would program (AI based module). By means of the comparison of the different categories of irrigation, the recommendation of good practices as well as the training about the main concepts, the users of Simulareg are able to learn to optimize the use of water maximizing their production.

What service(s) provided (different from example?
• Irrigation Management
• Digital irrigation training
• e-learning
• Knowledge storage

The relation with digitization?
Simulareg integrates a powerful recommendation, training and consulting software tool for transforming the platform in a virtual expertise, especially for deciding among alternative options in planning and managing irrigation. These simulations will provide a platform for training and conducting early rapid prototyping of use cases and scenarios.
Multiple data sources are integrated, stored and managed in a data infrastructure complemented for an analytical tool based in artificial intelligence algorithms.

Name customer, contact details
DARP (, gabriel.anzaldi@eurecat.org

Organization

Organizational form

(part of) Public organization (part of RTO, or university)

Turnover

0-250.000

Number of employees

1-9

Evolutionary Stage

In preparation

Geographical Scope

European

Funding

  • Horizon 2020
  • National basic research funding
  • National specific innovation funding
  • Regional funding
  • Private funding
  • Partner resources

Partners

Cluster of Agricultural Production Means in Catalonia (FEMAC)


Generalitat de Catalunya (DARP)


Community of food production technologies (COTPA)


Lleida Agri-food Science and Technology Park (PCiTAL)


University of lleida (UdL)

Technological competences

  • Internet of Things (e.g. connected devices, sensors and actuators networks)
  • Artificial Intelligence and cognitive systems
  • Location based technologies (e.g. GPS, GIS, in-house localization)
  • Data mining, big data, database management
  • Simulation and modelling
  • Gamification
  • Software as a service and service architectures
Last updated: 29/01/18 09:43