Transcript Challenge - ICT-AGRI

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
ICT-AGRI 2: project goal
Identify needs
and solutions
Action plan for
implementation of the
Strategic Research Agenda
Implementation in
transnational calls
Implementation in EU
and national initiatives
Three annual repeats
Topic 2015-2016: Farm
Management Information Systems
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.
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.
4 main FMIS subdomains
Across the main farm management topics
1.
2.
3.
4.
Precision crop management
Precision livestock management
Building and facilities management
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
1: Information collection mechanisms
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.
2: Information aggregation and analysis
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.
3: Decision support and information exchange
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.
4: Platform implementation and adoption
Towards an Open Architecture!
Island
automation
Closed
ERP systems
Open
Architecture
internet backbone
skip this step?
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!