1. ICT-AGRI-2 FP7 ERA-NET 2014 -17 Information and Communication Technologies and Robotics for Sustainable Agriculture Action plan 2015-2016 for implementation of the ICT-AGRI SRA Towards the second ICT-AGRI2 call Jack Verhoosel The Netherlands Organization for Applied Scientific Research ict-agri.eu

2. ICT-AGRI 2: project goal Action plan for implementation of the Strategic Research Agenda Identify needs and solutions Implementation in transnational calls Three annual repeats Implementation in EU and national initiatives

3. Topic 2015-2016: Farm Management Information Systems

4. FMIS current problems For interoperability ICT components used within the same farm enterprise: have partly overlapping and partly unique services, functions and interfaces, are missing required application services, functions and interfaces have separated data repositories and have inadequate and incomplete data exchange. Most of the available ICT components are lacking both technical and semantic interoperability, resulting in data sharing issues and non-coherent user interfaces Thus, they do not sufficiently support the monitoring, planning and control processes to enable precision- or smart farming.

5. FMIS overall challenge Overall challenge: integration of all kind of IT systems that make up an FMIS: handling the increasingly large amounts of data, especially from all kind of agricultural equipment interoperability between various systems at farm level and in the whole supply chain network surrounding the farm standardization of data overcome the small scale and the regional focus of software development and comply with national or regional differences in farming practices.

6. 4 main FMIS subdomains Across the main farm management topics 1.Precision crop management 2.Precision livestock management 3.Building and facilities management 4.Food chain management

7. 4 main FMIS innovation topics Across the information chain 1.Information collection methods to get access to information 2.Information aggregation and analysis techniques to derive intelligence 3.Decision support and information exchange mechanisms to take action 4.Platform implementation and adoption strategies for stimulation

8. Access to sensor data ▫Challenge: to collect data from sensors of various types to be used further down the information chain using sensors on farm equipment, plants, cows, satellite images and weather. ▫Examples:  wireless sensor networks to monitor pest counts to automatically activate and disrupt the mating patterns of pests  embedded wireless devices and soil monitoring systems for farmers to measure moisture, detect leaks and more efficiently manage energy usage, all in real-time  wireless sensor networks for lifestock cow management  sensor data to control micro-climate conditions in greenhouses ▫Restriction: built on widely available mobile networks and disseminated via mobile phone, monitoring and electronic controls to help farmers continually analyze the quality of their produces 1: Information collection mechanisms

9. Data collection from various sources ▫Challenge: to collect data from various sources other then sensors to enhance automated analysis and decision-making, e.g. data from crowd sources, such as pictures and even social media. ▫Examples:  Crowd sourcing for verification of local weather information along with soil humidity  Tracking pest and disease outbreaks to help in identification and removal of different types of weeds through crowd sourcing  Ccompiling field plans for suitable grazing trough crowd sourcing  Updating existing geospatial data with pictures and video recorded on mobile devices using GPS and input the field sampling information  Crowd sourcing to allow access to remote farmers and their inputs  Collection and reporting of information on crises, disturbances, and other events by mobile phone and updating the information on Google Maps 1: Information collection mechanisms

10. Information standardization ▫Challenge: to standardize the information measured by sensors and collection systems to be able to compare output equally ▫Examples:  Insight and automation to enable farmers to program exactly what and where each piece of equipment will plant, fertilize, spray and harvest for an area as small as one by three meters.  Combine produces from different farmers for collective buying and selling  Definition of good agricultural practices based on standardized criteria  Bringing data from multiple sources together to allow farmer access to information, promoting market access and farmer collectivization  Embed connectivity into agricultural equipment through fleet telematics solutions, for farmers to track their machines and analyze actionable data in real-time. 1: Information collection mechanisms

11. Big data analysis for pattern recognition ▫Challenge: to define and operationalize an infrastructure to analyze and visualize the combination of large amounts of data from various different sources for farmers. ▫Examples:  Big data analysis on existing information sources on the farm to learn from historic data on crop and/or cattle.  Satellite data coming from open data sources from government or open weather information sources as a basis for decision-making.  Deciding on crop type versus land area  Looking for patterns between feed and milk production  Analysis of CO2-efficienciy of buildings  Analysis of historic or forecast of demand/supply for products. ▫Restriction: use existing analysis techniques: text mining methods and tools, data clustering techniques, machine learning, data analysis workflows and data mining. 2: Information aggregation and analysis

12. Semantic alignment for aggregation of data ▫Challenge: to define mechanisms that enable the semantic mapping of similar but slightly different terms in the various data sources in order to combine them in a semantically correct way. ▫Examples:  Define knowledge models and ontologies for data in existing data sources using existing ones like AgroVoc and AgroRDF to open them up for broader use.  Common datamodels for mapping between existing sources in order to enable more broader answering of big data questions on crop and livestock management.  Use of methods for automatic enrichment of data sources with metadata. ▫Restriction: build on top of existing vocabularies and ontologies, such as AgroVoc, AgroRDF, GoodRelations, etc. 2: Information aggregation and analysis

13. Information ownership ▫Challenge: to deal with ownership of data, licensing for data usage, define new business models for data sharing, cost-benefit analysis for the aggregation of data and allow for anonymous as well as open data. ▫Examples:  Data collected for supply chain management purposes or for machine evaluation purposes may not belong to the farmer or even reside on his computer system.  Data curation agreements where the data is held by a third party on behalf of the farmer and made accessible for app developers to develop apps to integrate systems.  Farmers want to control whom can see and use their data. The IT sector has mechanisms and tools for the development of systems that ensure this. These should be used in the agricultural sector to improve innovation on this topic.  Access to good quality public data. 2: Information aggregation and analysis

14. Decision support systems ▫Challenge: to build decision support systems that add value based on automatically processed sensor data. ▫Examples:  Due to the continuing increases in farm scales, making well-informed management decisions becomes increasingly difficult without using sensors and ICT.  Current sizes of farms make manual checking of crop and livestock health and growth a daunting task.  In order to keep farms manageable, data-driven ICT-based decision support systems will increasingly replace this manual work. These systems can perform a variety of tasks (e.g. early warning systems, economic decision support, provision of traceability of farm products).  Decision strategies for operation and dealing with risks by standardizing operating procedures and taking Ppreventive measurements to deal with risks 3: Decision support and information exchange

15. Standardization of high quality data ▫Challenge: to standardize data for efficient exchange between farms and all other stages/stakeholders in the agricultural supply chain. ▫Examples:  A good choice of data platform (preferably an existing, public platform) should be made to base the standardization on in crop farming and livestock farming.  Standardization should focus on high data quality, i.e. the provision of adequate metadata for the various datasources and information systems already in use in crop and livestock farming.  Information interoperability for traceability of food products in the supply chain  Data exchange with stakeholders in government, research and service providers for the collection of information for reporting purposes. 3: Decision support and information exchange

16. Indicator development ▫Challenge: to develop algorithms in order to provide for correct decision support based on indicators that encapsulate multiple criteria. ▫Examples:  Focus on multiple criteria: ideally economic, environmental and social criteria in order to take into account all aspects of the farm, crop/cattle, operations and buildings/facilities.  Make these indicators transparent and well-documented, so they can be used for all products and by all stakeholders across the supply chain.  Compliance with local laws on traceability and data exchange is required. 3: Decision support and information exchange

17. Towards an Open Architecture! internet backbone Island automation Closed ERP systems Open Architecture skip this step? 4: Platform implementation and adoption

18. Platform for modular, smart services/applications

19. Open standard interfaces

20. Call requirements on components in ICT-AGRI Devices should comply with international standards (e.g. ISOBUS) Data messages should comply with existing national and international data standards Applications should have an open API so that other applications are able to collaborate Reference process models should be provided that describe farming business processes providing guidelines for configuration of different ICT components and services

21. Additional call requirements Easy-to-use user interfaces Web-based components for future IoT integration Farmers should be involved in application development Platforms/applications should have a consistent business model Support for developers and service providers in application configuration and integration Focus should be on harmonizing existing platforms and standards instead of developing new ones (FiSpace, Farm365Net, Datalab) Compatibility should be ensured with products from other/different suppliers and demonstrated via plugfests Contribution to standardization is welcome ICT-AGRI should define a separate project/workgroup that supports, facilitates and safeguards all these requirements Also provide a physical infrastructure/middleware layer?

22. Let’s discuss! Discussion about the innovation topics: 1.Do you agree with the structure? 2.Did we miss important topics? 3.Which emphasis in our call?

23. More information from our website: ict-agri.eu Thank you for your attention!