ICT-AGRI-2 FP7 ERA-NET 2014 -17Information and Communication Technologies and Robotics for Sustainable Agriculture

Action plan 2015-2016 for implementation of the ICT-AGRI SRATowards the second ICT-AGRI2 call

Jack Verhoosel

The Netherlands Organization for Applied Scientific Research ict-agri.eu

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

Topic 2015-2016: Farm Management Information Systems

FMIS current problemsFor 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.

FMIS overall challengeOverall 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.

4 main FMIS subdomains

Across the main farm management topics

1. Precision crop management2. Precision livestock management3. Building and facilities management4. Food chain management

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

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

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

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

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

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

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

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

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

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

Towards an Open Architecture!

internet backbone

Islandautomation

ClosedERP systems

Open Architecture

skip this step?

4: Platform implementation and adoption

Platform for modular, smart services/applications

Open standard interfaces

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

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?

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?

More information from our website:ict-agri.eu

Thank you for your attention!