Large ICT-projects in Agri-Food in Europe
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This is a presentation about the background, development and state-of-the-art of large ICT-projects in Agri-Food that are going on in Europe: Internet of Food and Farm 2020 (IoF2020) and SmartAgriHubs.
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Large ICT-projects in Agri-Food in Europe
- 1. Large ICT projects in Agri-Food in Europe: IoF2020 and SmartAgriHubs Dr. Sjaak Wolfert Strategic Senior Scientist at Wageningen University & Research McGill University, Montreal, Canada, 27 Jun. 2019
- 2. Wageningen University & Research: Two Partners Wageningen University & Wageningen Research
- 3. To explore the potential of nature to improve the quality of life Wageningen University & Research Academic research & education, and applied research 5,800 employees (5,100 fte) 12,000 BSc/MSc students (>120 countries) 2,000 PhD students > 30 locations in NL, also global satellites Turnover about € 650 million #1 Agricultural Sciences 14x
- 4. Campus ecosystem: science with impact... University • Education and training • Fundamental and strategic research Research institutes • Strategic and applied research • Pre-competitive & confidential projects Spin-offs / SMEs • Innovation • Entrepreneurship • New companies (Inter)national companies • R&D departments • Shared facilities • Joint activities
- 5. European Public-Private Partnership ICT-projects 2011-2013: SmartAgriFood - a FIWARE-based conceptual architecture and prototype applications (5 M€) 2013-2015: FIspace – B2B business collaboration platform for agri-food & logistics (+ apps) (13.5 M€) 2014-2016: Accelerators: SmartAgriFood2, FInish, FRACTALS (~17 M€) - 125 apps/start-ups based on FIWARE/FIspace Sep. 2016: FIWARE Foundation established with 3 verticals: Smart Cities, Industry and Agri-Food 2017-2020: IoF2020 – The Internet of Food and Farm (30 M€) - IoT large-scale pilot for smart farming and food security 2018-2022: SmartAgriHubs – Connecting the dots to unleash the innovation potential for digital transformation of the European Agri-Food sector (20 M€)
- 6. BIG DATA Smart Farming: context-aware system CONTROL SENSING & MONITORING ANALYSIS & PLANNING SMART SMART SMART
- 7. Involving entire supply chain and beyond Smart Farming Smart Logistics Tracking & Tracing Consumer trends Domotics Health Fitness/Well-beingPersonalized
- 8. 1. Decision-Making for Business/Consumers 2. Food Integrity HealthFood Safety Environment Nutrition Food SecurityClimate 4. Decision-making for Society 110100101111001110000111011010101010000110 110100101111001110000111011010101010000110 Smart Sensing & Monitoring Smart Control Smart Analysis & Planning Cloud Computing Big Data Analytics Internet of Things Linked Data Artificial Intelligence Blockchain Technology 3. Science & Technology Digitization of Agri-Food: 4 areas coming together
- 9. Creating a collaborative infrastructure Scenario: get expert advice for spraying to handle disease on tomatoes State AuthorityFranz Farmer Ed Expert Spraying (follow advice) Create Advice Approval Request Advice CollaborativeBusinessProcess 1 2 3 FIspace App ‘Weather Information’ FIspace App ‘Spraying Expert Advice’ FIspace App ‘Spraying Certification’ Back-EndSystems Farm Management Systems Sensor Network in the Greenhouse Agronomist Expert System Regulations & Approval System product type, etc. sensor data (access details) suggested chemical advice details certification details 9
- 10. The Battlefield of Data for Farming and Food Farming Data Food Data See: Wolfert et al., Agricultural Systems 153 (2017) 69–80 Processors Ag Business Tech Companies Tech Start-up Tech Start-up Ag Tech Retail Venture Capitalists Data Start-up Data Start-up
- 11. Governance and business model issues 11 Code of Conduct
- 12. 12 Interview with Johan Bouma in Resource 4 Oct. 2018 p. 18-19 1. Multidisciplinarity 2. Collaborative process 3. Agile development
- 13. ECOSYSTEM & COLLABORATION SPACE ProjectCoordination& Management A multidisciplinary, collaborative, agile approach Trials/Use Cases: Knowledge & App development Lean multi-actor approach 3. EVALUATION 1. CO-DESIGN 2. IMPLEMENTATION P1 P2 LARGE SCALE P3 Data Science & Information management Business Modelling, Governance & Ethics Ecosystem Development
- 15. Objective: Large-scale uptake of IoT in the European farming and food sector • Business case of IoT • Integrate and reuse available IoT technologies • User acceptability of IoT • Sustainability of IoT solutions 15 Internet of Food and Farm 2020 Innovation Action: 2017 - 2020 30 M€ funding by DG-CNCT/AGRI
- 16. 19 + 14 use case projects Source: www.iof2020.eu/trials
- 17. Soil map based variable rate applications and machine automation in potato production UC1.1. WITHIN-FIELD MANAGEMENT ZONING Coordinators: Peter Paree (ZLTO) & Corné Kempenaar (WUR)
- 18. SOIL MAP SERVICE VARIABLE RATE APPLICATION MAP AUTOMATION & MACHINE COMMUNICATION Product Impressions
- 19. Major Challenge Here is what we aim to improve (KPIs) Yield by better plant distribution Variable planting distance map – Validation in 2017 and 2018. Nov. 2018 portal where maps can be ordered. Variable rate herbicide use map - Validation in 2016 and 2017. May 2018 portal where maps can be ordered. Quality by better plant distribution Reduction pesticide use Core Product Features Variable Rate Application Map Service Customer & Provider Uses soil maps and agronomic knowledge to create crop management task map based on variability in soil characteristics like organic matter and/or clay content, water storage capacity, tramlines, shade, etc.. Smart application of resources: seeds, pesticides, fertilizers +4% +5% -23% Better distribution of plants leads to +5% kilos and +5% better quality (more potatoes in desired size). Taking soil characteristics for weed growth into account: -23% less herbicide and +2% more yield. Enriching canopy index with soil characteristics lead to -10% less additional N fertilizer (2nd phase). These values derive from comparison of a standard farm’s performance prior to the installation of our system and after. Reduction fertilizer use -10% Product Factsheet Existing variable rate maps are often based on tweaking expert judgement and lack a certain level of precision in tasking / lack of validation. Farmers and advisors Price per unit, added value LoonwerkGPS, soil analysis labs, FMIS providers VRA additional N spraying June 2018 on Growth + Soil Maps. High spatio-temporal monitoring dashboard
- 20. IoT tools for sustainable wine production, wine quality management and shipping monitoring UC3.2. BIG WINE OPTIMIZATION Coordinators: Mario Diaz Nava, ST Microelectronics
- 21. Multi-actor approach January 1 2017
- 22. IoT Product Impressions sensors in the vineyard display devices, agronomic parameters and weather forecast Temperature/RH logger with data transmission NIR spectrometer % alc., sugar, etc.
- 23. PART OF THE BUSINESS MODEL ‘PROCESS2WINE’
- 24. Keep your herd healthy with an artificial intelligence monitoring system UC2.2. HAPPY COW Coordinators: Niels Molenaar, Connecterra
- 25. Product Impressions Intelligent Dairy Farmer’s Assistant in practice
- 26. Translating dairy cow behaviour to management information that helps a farmer to improve farm efficiency and animal health. Major Challenge Here is what we aim to improve (KPIs) Calving interval 305 day milk production Number of days treated with antibiotics Customer & Provider Ida a ‘farmers assistant’ based on artificial intelligence; it helps the farmer to keep the herd healthy. Dairy farmers -3% +1% -0.5%7,5 €/cow/month Detect oestrus with 80% accuracy Detection of health problems. Predict the start of calving Rank cows based on their feed efficiency Track management problems based on herd behaviour. Insights on dairy herds for partners like veterinarians and dairy processors with. Core Product Features Tracks cow behaviour and learns from the observed patterns to advise the farmer. Product Factsheet IDA: the Intelligent Dairy farmers Assistant
- 27. IOF2020 ECOSYSTEM & COLLABORATION SPACE WP1ProjectCoordination& Management GENERIC APPROACH & STRUCTURE WP2 Trials/Use cases: Knowledge & App development Lean multi-actor approach 3. EVALUATION 1. CO-DESIGN 2. IMPLEMENTATION P1 P2 LARGE SCALE P3 WP3 IoT Integration WP4 Business Support WP5 Ecosystem Development
- 28. TECHNICAL / ARCHITECTURAL APPROACH Use case architecture Use case IoT system developed Use case IoT system implemented Use case IoT system deployed USE CASE REQUIREMENTS IoT reference architecture instance of IoT catalogue Reusable IoT components reuse IoT Lab Reference configurations & instances reuse Collaboration Space shared services & data ProjectlevelUsecaselevel sustain reuse
- 30. Business support Different business models will be tested to identify the most successful and sustaining ones BUSINESS MODELS Buying and selling a product is te best service. MARKET STUDY Develop standard procedures and guidelines to handle sensitive information and to protect IP PRIVACY GUIDELINES Calculate costs savings and effects on revenue development & financing plans for farmers KPI & IMPACT
- 31. FOSTERING IOT-ECOSYSTEM: IMPACT • 33 IoT architectures implemented • 19,500 IoT devices • 80 IoT solutions • 20-25% more sustainable production • 100 test beds & demosites • 500 new stakeholders involved • Use cases: TRL 5 8 • 80% user satisfaction • 95% viable business models • 60 M€ industry investments • 30 M€ new business by IoT developers
- 32. 1. Decision-Making for Business/Consumers 2. Food Integrity HealthFood Safety Environment Nutrition Food SecurityClimate 4. Decision-making for Society 110100101111001110000111011010101010000110 110100101111001110000111011010101010000110 Smart Sensing & Monitoring Smart Control Smart Analysis & Planning Cloud Computing Big Data Analytics Internet of Things Linked Data Artificial Intelligence Blockchain Technology 3. Science & Technology Re: Digitization of Agri-Food: 4 areas coming together
- 33. Decision-Making Food Integrity HealthFood Safety Environment Nutrition Food SecurityClimate Public decision-making 110100101111001110000111011010101010000110 110100101111001110000111011010101010000110 Smart Sensing & Monitoring Smart Control Smart Analysis & Planning Cloud Computing Big Data Analytics Internet of Things Linked Data Artificial Intelligence Blockchain Technology Science&Technology But...developments are still very fragmented
- 34. 34 Consolidate and foster EU-wide network of Ag Digital Innovation Hubs to enhance digital transformation for sustainable farming and food production Region and Sector Specific expertise Digital Transformation of the European Agri-Food Sector Technology expertise Business model expertise SmartAgriHubs’ Overall Objective
- 35. 35 Where IoF2020 stops... and SmartAgriHubs continues ECOSYSTEM & COLLABORATION SPACE ProjectCoordination& Management Trials/Use Cases: Knowledge & App development Lean multi-actor approach 3. EVALUATION 1. CO-DESIGN 2. IMPLEMENTATION P1 P2 LARGE SCALE P3 Data Science & Information management Business Modelling, Governance & Ethics Ecosystem Development
- 36. 36 Digital Innovation Hub: local one-stop shop Incubators Government Cooperatives Farmer communities Investors Others Advisories Research organisations Start-ups Education & training institutes Large companies Industry associationsCompetence Center Other Competence Centers Orchestrator Other DIHs Innovation Experiments
- 37. 37 DIH innovation services Ecosystem • Community building • Strategy development • Ecosystem learning • Project development • Lobbying Technology • Strategic RDI • Contract research • Technical support on scale-up • Provision of technology infrastructure • Testing and validation Business • Incubator/accelerator support • Access to finance • Skills and education
- 38. 38 The 5 basic concepts of SmartAgriHubs Innovation service maturity model for DIHs Innovation Portal Innovation Experiments Layered network of DIHs & CCs in Regional Clusters Digital Innovation Hubs Competence Centres
- 39. 39 SmartAgriHubs’ challenge: expand! Ecosystem 108+ Partners Involved covering all EU 68 partners are SMEs 54% of budget allocated to SMEs 140 DIHs in the existing Network covering all 28 Member States Regional Approach 9 Regional Clusters Attract 260 New DIHs Digital Innovation hubs Impact 30M additional funding Mobilized from other sources(public, regional, national and private) 80 new digital solutions Introduced into the market 2M Farms involved in digitisation Open Calls 6M Euros distributed through Open Calls 75% Open Call budget to SMEs 70 New Innovation Experiments 28 FIEs 22 Countries involved 13 Cross-border collaboration FIEs (47%) Flagship innovation experiments Arable 8 (28,6) Fruit 4 (14,2%)Vegetables 5 (17,8%) Livestock 10 (35,7%) Aquaculture 1 (3,5%) 5 sectors
- 40. Thank you for your attention! More information: sjaak.wolfert@wur.nl nl.linkedin.com/in/sjaakwolfert/ Twitter: @sjaakwolfert http://www.slideshare.net/SjaakWolfert 40
Notes de l'éditeur
- These are some flagship projects that I just want to mention before I move to FarmDigital
- This has become our general project approach in many projects...
- This slide provides an overview of the project aim and objectives.
- Through these projects we have developed a success formula in approaching the challenge of ICT and Information Management in Agri-Food : Trials and use cases form the core, in which we jointly develop as research and business organisations, knowledge and application through a lean multi-actor approach This means that we quickly develop minimum viable products with involvement of all relevant stakeholders and upscale these through several cycles of development In parallel we create synergy by Technical integration: open architectures, standard that can be used as generic building blocks in the trials and use cases Governance and business modelling: solve issues that arise from the trials and use cases regarding ownership, privacy, trust, etc. and support the businesses in developing sustainable business plans for the apps, services and organization structures that are being developed Ecosystem Development – support the trials and use cases in embedding their solutions in global ecosystems and upgrading them to a large scale Project coordination and management is trivial, but we have shown that Wageningen University and Research is very capable to fulfil this role in large public-private projects This integrated approach will guarantee long-term, sustainable results from these projects.
- IoF2020 believes that it is important for a large scale take‐up to maximize synergies across multiple use case systems. As a consequence, much attention is paid to ensuring the interoperability of multiple use case systems and the reuse of IoT components across them. The figure shows the architectural approach to achieve this during design, development, implementation and deployment. To enable reuse of components, IoF2020 will provide a catalogue of reusable system components, which can be integrated in the IoT systems of multiple use cases of the project. It will include as much as possible existing components from previous and running projects and (open source) initiatives, including FIWARE, FIspace, etc.
- In this example WUR as a whole acts as a DIH delivering several services that orchestrate various players outside the DIH. Ultimately, this should result in newly, funded Innovation Experiments, where several of these players are collaborating on digital innovations. WDCC delivers various competences within the WUR-DIH to (i) facilitate the orchestration process, (ii) setup an innovation experiment and (iii) execute innovation experiments An example: A bright start-up has a splendid data-driven product that is expected to help farmers to improve crop disease management. However, it is still a prototype that needs to be upgraded to a real, marketable product in an innovation experiment. They knock on the door of the WUR-DIH for help. WUR-DIH uses its network of farmers and cooperatives to get end-users interested to experiment and validate the product. There’s a also a need for an appropriate data infrastructure that is robust and compliant with the state-of-the-art security standards. WDCC brings in their knowledge and network of (large) ICT companies to advise on the right infrastructure and helps to choose (i). WUR-DIH also does matchmaking in their network of public and private funders to find the financial resources to carry out the innovation experiment. They help the start-up and the established multi-actor network to write a high-quality project plan. This plan requires a good data management plan. WDCC is delivering a services to write a good data management plan (ii). Finally the innovation experiment is conducted and WDCC helps to analyse the data by connecting the right data scientists from WUR to the innovation experiment (iii) Potentially, WDCC can also have this function for other DIHs in the whole SmartAgriHubs network of DIHs, as other competence centers can also be involved in the WUR-DIH.