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The Internet of Food and Farm

Presentation for Alumni of the Van Hall Larenstein University of Applied Science.

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The Internet of Food and Farm

  1. 1. The Internet of Food and Farm Sjaak Wolfert Sr. Scientist Information management & ICT in Agri-Food Alumni-bijeenkomst Precisielandbouw, HVHL, Leeuwarden, 6 December 2016
  2. 2. Our mission and approach Support the agri-food business in implementing ICT solutions by:  Analysis – what are the ICT challenges for your business/sector?  Design – how should the ICT-solution look like? (based on reference architecture/infrastructure)  Iterative implementation – by developing pilots and prototypes, mostly in sector-wide or beyond-sector public-private projects
  3. 3. everything anything everybody ICT as a driver for innovation Mobile/Cloud Computing Location-based monitoring Social media Internet of Things Blockchains (?) = Big Data = The New Oil? anywhere We ain’t see nothing yet! everywhere
  4. 4. How will ICT drive innovations in agri-food? Transport Current issues How more ICT and data can contribute to this Which innovations and new business concepts are possible ? Major global challenges Transpo rt Transport Input industry Farmer Food Processor Retail / consumerSoftware Provider Logistic Solution Provider
  5. 5. Current issues Transport Transpo rt Transport Input industry Farmer Food Processor Retail / consumerSoftware Provider Logistic Solution Provider High-Tech Interoperability Cost price & Quality New services Consumer demands Loyalty On-line shopping
  6. 6. Major global challenges Transport Transpo rt Transport Input industry Farmer Food Processor Retail / consumerSoftware Provider Logistic Solution Provider Food & Nutrition Security Sustainability Health Food Safety & Transparency
  7. 7. How can ICT add value?
  8. 8. FI-Ware enabled Cloud Platform Cloud Information systems SmartAgriFood’s conceptual cloud architecture sensors actuators data sources (‘Internet of Things’) local Information systems App store Services Spraying Advisory Services Meteorological Service State and Policy Information Service Consumer Food safety service E-agriculturist Service for spraying potatoes Machine Breakdown Service Transport User’s devices Other sources
  9. 9. smart sensing & monitoring smart analysis & planning smart control Smart Farming: closing the cyber-physical management cycle BIG DATA
  10. 10. Ag Equipment: mobile networks • Tractor and Implement are acting as one network • Always connected! Cloud / Internet
  11. 11. PAGE11 WWW.CONNECTERRA.IO Our solution: The Dairy Monitor Complete animal healthcare in one device. Based on sensor data we are able to provide the farmer with valuable insights and actions Heat and Health Detection Farmer Insights Location Services No more graphs to understand, the system tells you what to do! Track animal movements and grazing habits to enable organic certifications Early detection of heat and health issues improves productivity by 20% Sensors Algorithms run in the cloud Insights & actions
  12. 12. Involving the entire supply chain network and beyond Smart Farming Smart Logistics tracking/& tracing Source: Hisense.com Domotics Health Fitness/Well-being
  13. 13. Cloud Event Management System Location A Location B Virtual Plant Virtual Location A Virtual Location B Environ ment update Plant location update Environ ment update
  14. 14. Tracking and Tracing and Meat Awareness (TTAM)
  15. 15. What needs to happen behind the scenes...
  16. 16. Smart Food Awareness To satisfy needs of each consumer by providing transparent and tailored information about agri-food products. I am a Royal Gala apple from south Spain, I was grown without pesticides following organic farming criteria, I have been here for 1 day, my carbon footprint is 1,2 kg CO2e. I am a self- conscious consumer that wants to now where my food comes from and how it is produced
  17. 17. App store From conceptual architecture & prototypes to a real software platform and Apps Services sensors actuators data sources (‘Internet of Things’) Local ISs Spraying Advisory Services Meteorological Service State and Policy Information Service Consumer Food safety service E-agriculturist Service for spraying potatoes FI-Ware enabled Cloud Platform Machine Breakdown Service User’s devices Other sources Cloud IS Transport I2ND IoT IoC IoS S&T GENERIC ENABLERS Base Technologies Validation T270: Security, Privacy, Trust Framework: SPT (KOC) T250: System & Data Integration (ATOS) T240: B2B Collaboration Core (IBM) T230: App Store (IBM) T220: User Front-End (ATOS) T260:OperatingEnvironment (IBM) T280:SoftwareDevelopmentToolkit: SDK(ATOS)
  18. 18. FIspace platform High Level Architecture I2ND IoT IoC IoS S&T GENERIC ENABLERS Base Technologies Validation 1. Crop Protection Information Sharing 2. Greenhouse Management & Control 3. Fish Distribution & (Re-)Planning 4. Fresh Fruit and Vegetables QA 5. Flowers & Plants SC Chain Monitoring 6. Meat Information Provenance 7. Import & Export of Consumer Goods 8. Tailored Information for Consumers Trials: Security, Privacy, Trust Framework: SPT System & Data Integration B2B Collaboration Core App Store User Front-EndOperatingEnvironment SoftwareDevelopmentToolkit:SDK
  19. 19. Example: Spraying Scenario Scenario: get expert advice for spraying to handle disease on tomatoes State AuthorityFranz Farmer Ed Expert Spraying (follow advice) Create Advice Approval Request Advice CollaborativeBusinessProcessBack-EndSystems Farm / GH Management Systems Sensor Network in the Greenhouse Agronomist Expert System Regulations & Approval System 1 2 3 FIspace App ‘Weather Information’ FIspace App ‘Spraying Expert Advice’ FIspace App ‘Spraying Certification’ product type, etc. sensor data (access details) suggested chemical advice details certification details 19
  20. 20. Redefining Industry Boundaries (1/2) (according to Porter and Heppelmann, Harvard Business Review, 2014) 20 3. Smart, connected product + + + 2. Smart Product 1. Product
  21. 21. Redefining Industry Boundaries (2/2) (according to Porter and Heppelmann, Harvard Business Review, 2014) 21 5. System of systems farm management system farm equipment system weather data system irrigation system seed optimizing system field sensors irrigation nodes irrigation application seed optimization application farm performance database seed database weather data application weather forecasts weather maps rain, humidity, temperature sensors farm equipment system planters tillers combine harvesters 4. Product system
  22. 22. Redefining Industry Boundaries (2/2) (according to Porter and Heppelmann, Harvard Business Review, 2014) 22 5. System of systems farm management system farm equipment system weather data system irrigation system seed optimizing system field sensors irrigation nodes irrigation application seed optimization application farm performance database seed database weather data application weather forecasts weather maps rain, humidity, temperature sensors farm equipment system planters tillers combine harvesters 4. Product system Your company
  23. 23. Redefining Industry Boundaries (2/2) (according to Porter and Heppelmann, Harvard Business Review, 2014) 23 5. System of systems farm management system farm equipment system weather data system irrigation system seed optimizing system field sensors irrigation nodes irrigation application seed optimization application farm performance database seed database weather data application weather forecasts weather maps rain, humidity, temperature sensors farm equipment system planters tillers combine harvesters 4. Product system Your company Farmer: how many platforms must I use? Developer: on how many platforms should I offer my solution? Platform owner: how many connections do I need to maintain?
  24. 24. Battlefield of IoT, Big Data and Farming Farm Farm Farm Farm Data Start-ups Farming Cooperatives Open Ag Data Alliance ... AgBusiness Monsanto Cargill Dupont ... Tech Companies Google IBM Oracle ... Ag Tech John Deere Trimble Precision planting ... Tech Start-upsFarm Tech Start-ups Data Start-upsVenture Capital Anterra Founders Fund Kleiner Perkins ... Farm
  25. 25. USA start-ups in different areas
  26. 26. Discussions  Am I owning my own tractor? (IPR on software)?  Do I own my data? Who has access?  Does the government have insight?  Do certain companies get much power in the market?  Is there a lock-in situation? Can I transport my data?  Do I become a franchiser carrying the risks and limited returns? Code of Conduct
  27. 27. Towards business/software ecosystems
  28. 28. New Business Models based on Big Data See: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into profit“, 2014  Basic data sales ● commercial equivalent of open data (e.g. FarmMobile)  Product innovation ● use data to improve your product (machinery industry, e.g. John Deere, Lely’s milking robots)  Commodity swap ● data for data (e.g. between farmers and (food) processors to increase service component)  Value net creation ● pool data from the same consumer (e.g. AgriPlace)  Value chain integration ● use data to control the whole chain (e.g. Monsanto’s Fieldscript)
  29. 29. Commodity swap - data for data Many farmsIndividual farm Data Benchmark Analytics
  30. 30. Basic data sales “Farmers think their trust is violated” Their data goes to multinationals that promise high future yields based on big data, while farmers have to pay for everything How does it work? - A ‘box’ collects all data - Data is stored in a cloud - Data is being marketed/invested - Farmer gets a share of profit
  31. 31. Value Chain Integration: Monsanto’s FieldScripts PRESCRIPTIVE FARMING based on PRECISION AGRICTULTURE
  32. 32. Farmers Business Network • Owned by farmers • Funded by Google Ventures 2015: 7 million acres ground Assessment of 500 seeds and 16 crops Costs for farmer: $ 500 / year.
  33. 33. DATA-FAIR: Open Software Ecosystem Stakeholders Platforms Apps + services Knowledge models Security, Privacy, Trust Business models Data sharing Possible example of open collaboration Farmer Open Architecture & Infrastructure Event-driven, Configurable, Customizable Standards & Open Datasets Real-time data sharing IoT layer
  34. 34. Internet of Food and Farm 2020 2017-2020 - 70+ partners - 30M€ funding
  35. 35. IoF2020 – Large Scale Pilot
  36. 36. Wrap-up  Agri-Food chains become more technology/data-driven ● Probably causes major shifts in roles and power relations among different players in agri-food chain networks ● Open infrastructure and software development and business models are key issues  Two extreme scenarios: 1. Strong integrated supply chain in which farmer is franchiser/contractor with limited freedom 2. Open collaboration network in which a farmer is empowered through easier switch between suppliers and customers Reality somewhere in between? Farmer Farmer
  37. 37. Thanks for your attention! Questions? More information sjaak.wolfert@wur.nl nl.linkedin.com/in/sjaakwolfert/ Twitter: @sjaakwolfert http://www.slideshare.net/SjaakWolfert

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