AIOTI
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Report
AIOTI WG06 – Smart Farming and Food Safety
2015
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Executive Summary
Summary of the Report including major pilot characteristics, links to other WGs and
reccommendations
The executive summary should be less technical in terminology than the report and be one page
long.
The Executive Summary should be organized according to the categories covered in the report
[To be completed in the final stage of the preparation of the Report]
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Table of content (to be defined in function of the chapters selected)
Table of contents
CHAPTERS ................................................................................................................................................................. 4
1
SCOPE AND FOCUS OF THE WG [1-2 PAGES] ......................................................................................... 4
1.1
1.2
2
VISION ......................................................................................................................................................... 4
OBJECTIVES .................................................................................................................................................. 4
MAPPING OF EXISTING INITIATIVES IN THE RELEVANT AREA OF THE WG [5-6 PAGES] ..... 6
2.1
2.2
2.3
EXISTING INITIATIVES .................................................................................................................................. 6
RELEVANT USES CASES FOR A LSP ON SMART FARMING AND FOOD SAFETY ............................................ 10
RECOMMENDATIONS ON THE FEASIBILITY OF A LSP COVERING SMART FARMING AND FOOD SAFETY ...... 13
3
INVESTIGATION OF THE TECHNOLOGICAL DIMENSION FOR THE LARGE SCALE PILOT [56 PAGES] ................................................................................................................................................................... 14
3.1
MAPPING OF RELEVANT TECHNOLOGIES AND STANDARDS APPLICABLE TO SMART FARMING AND FOOD
SAFETY 14
3.2
REQUIREMENTS FOR THE SELECTION OF TECHNOLOGIES, STANDARDS, AND INTEROPERABILITY FOR THE LSP
15
3.3
RECOMMENDATION ON THE FEASIBILITY AND REPLICABILITY OF THE LSP ................................................ 16
4
RECOMMENDATIONS FOR THE TESTING OF BUSINESS MODELS AND OF USER
ACCEPTABILITY [5-6 PAGES] ............................................................................................................................ 18
4.1
4.2
RECOMMENDATIONS FOR THE TESTING OF BUSINESS MODELS .................................................................... 18
RECOMMENDATIONS FOR THE TESTING OF USER ACCEPTABILITY ............................................................... 18
5
INVESTIGATION OF THE OPERATIONAL DIMENSION FOR THE LARGE SCALE PILOT [2-3
PAGES] ...................................................................................................................................................................... 19
5.1
5.2
5.3
5.4
GOVERNANCE OF THE CONSORTIUM ........................................................................................................... 19
FACILITATING COLLABORATION ................................................................................................................. 20
SUSTAINABILITY OF THE PILOT BEYOND THE FUNDING PERIOD .................................................................. 20
FROM THE PILOT TO THE MARKET............................................................................................................... 20
6
NEXT STEPS [1 PAGE] .................................................................................................................................. 21
7
REFERENCES ................................................................................................................................................. 21
APPENDIX 1 - QUESTIONNAIRE FOR KNOWLEDGE GATHERING ON BUSINESS MODELS ............ 22
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Chapters
This version of the Recommendations Report of AIOTI WG 6 is a draft released on 07/09/2015.
Please provide your feedback and comments to the current editors via email
Luis Pérez-Freire, AIOTI WG6 Chairperson - lpfreire@gradiant.org
Lyse Brillouet, AIOTI WG6 Co-Chaiperson – lyse.brillouet@orange.com
Marcos Álvarez-Díaz - malvarez@gradiant.org
1
Scope and focus of the WG [1-2 pages]
Leading editor: Luis Pérez-Freire (Gradiant)
1.1
Vision
The scope of AIOTI WG6 covers all possible applications of the Internet of Things (IoT) to the domains of
smart farming and food safety that are suitable to become commercially viable in the future.
By smart farming we understand the application of data gathering, data processing, and automation
technologies, that jointly orchestrated allow for improved operation and management of a farm with respect
to standard operations. In this sense, smart farming is strongly related, but not limited, to the concept of
Precision Agriculture. Farming modalities may include the production of vegetables, cattle (including dairy
production) and others. IoT and smart farming applications could also be new levers to take into
considerations new trends in agriculture, such as back to traditional farming (small or complex spaces,
specific cultures and/or cattle…) and enhance a very respectful farming accordingly to European consumer
consciousness.
The term food safety refers to the prevention of foodborne illnesses, from food production to comsumption.1
Vision: To become the focal point of all EU-based stakeholders interested in exploiting the potential
benefits of the IoT in the domains of food production and food safety. AIOTI WG6 will bring together
European IT technology providers with stakeholders of the European ‘from farm to fork’ chain, to foster
the generation of a future market of commercially viable IoT-based solutions tailored to the needs of the
European agrifood sector.
Within AIOTI, WG6 foresees synergies and cooperation with some of the vertical WGs of AIOTI such as
WG 5 (Smart living environment of ageing well), and WG 10 (Smart environment –smart water
management), as well as with the horizontal WGs (WG1: IERC, WG2: Innovation Ecosystems, WG3: IoT
Standardization, WG4: Policy Issues).
1.2
Objectives
Globally, AIOTI WG6 on Smart Farming and Food Safety aims to detect the stakes the main players face
in these areas where IoT could bring a major advange and to define where in a short-term time frame (1836 months), a large scale pilot could demonstrate the whole interest to deploy such solutions (economic
conditions, tehnical feasibility, consumer adoption, industrial value for key players…).
The objectives of AIOTI WG6 are stated in the table below.
Table 1.1: Objectives of AIOTI WG6
1
AIOTI WG6 deals with food safety as described above, and not with food security, which rather relates to ensuring
that all the population has access to sufficient food and nutrients.
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Number
1
2
3
4
5
AIOTI
Description of the objective
To build a community of active stakeholders interested in the
application of IoT for farming and food safety.
Target: 30+ companies/entities
To deliver a report with recommendations towards the
implementation of a Large Scale Pilot on Smart Farming and Food
Safety
To detect, thanks to a pluridisciplinary participation, the trends and
disruptions farming and food production would have to face
To identify a number of benefits that IoT can bring
To identify a number of disruptive IoT-based solutions that could
have game-changing effects in the agrifood market chain
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Expected
completion
Q3/Q4 2015
Q3 2015
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2
Mapping of existing initiatives in the relevant area of the WG [5-6 pages]
Leading editor: Luis Pérez-Freire (Gradiant)
2.1
Existing initiatives
The following table summarizes the existing initiatives identified so far.
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Domain / application case coverage
http://ec.e
uropa.eu/e
ip/agricult
ure/en/co
ntent/mai
nstreamin
gprecisionfarming
EU
X
X
ERA-NETs ICT
Agri 1, ICT Agri 2
ERANET
projects
FP7
project
(FIWARE
based)
FP7
project
(FIWARE
based)
http://ww
w.ictagri.eu/
http://ww
w.smartag
rifood.eu/
EU
X
X
EU
X
X
FP7
project
(FIWARE
based)
http://quh
oma.com/
The SmartAgriFood project is part of
the Future Internet Public-Private
Partnership (FI-PPP) program and
addresses farming, agri-logistics and
food awareness as a use case for this.
FIspace is a business-to-business
(B2B) collaboration platform. It
works like a social network, like
LinkedIn or Facebook. Once
registered, contacting affiliates is
simple, secure and easy.
FIWARE funded marketplace for
efficient production of qualitative
Horticultures and their effective
marketization.
The ultimate aim of the FInest project
is to develop a Future Internet
EU
FIspace
QUHOMA
Finest
http://fisp
ace.eu/
http://ww
w.finest-
EU
X
X
X
EU
X
X
Available
TRL
(if
exists)
Livestock
farming
EIP
focus
group
Food safety /
health
/
traceability
Consumer
Plant
Farming
EIP-Agri
Precision Farming
SmartAgriFood
Short description
Retail
Website
Logistics
Type
Food
processing
Inititiative
Geographica
l coverage
Domain / application case coverage
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ppp.eu/
EFFIDRIP
enabled ICT platform for better
supporting and optimizing the
collaboration and integration within
international transport and logistics
business networks. This shall be
realized as a domain-specific
extension of the FI PPP Core
Platform
FP7
project
FP7
project
http://effi
drip.eu/
http://ww
w.agrixch
ange.org/
Precision
Livestock
Farming (PLF)
FP7
project
http://ww
w.euplf.eu/
EU
X
ALL-SMARTPIGS
FP7
project
EU
X
MUSETECH
FP7
project
http://ww
w.allsmartpigs.org/
https://w
ww.muset
ech.eu/
WssTP
H2020
project
http://wsst
p.eu/
WaterInnEU
H2020
http://ww
agriXchange
AIOTI
EU
agriXchange is a EU-funded
coordination and support action to
setup a network for developing a
system for common data exchange in
the agricultural sector.
The concept of MUSE-Tech project
is the integration of three High-End
sensing technologies (Photoacoustic
Spectroscopy, Quasi Imaging UVVis Spectrometry and Distributed
Temperature Sensing) in a versatile
Multi Sensor Device (MSD), for realtime monitoring (on-line or in-line)
of multiple parameters associated
with the quality and the chemical
safety of raw and in-process
materials.
WssTP is the European
Water
Supply and Sanitation Technology
Platform.
WaterInnEU’s primary vision is to
EU
X
X
EU
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X
X
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project
AIOTI
w.waterin
neu.org/
create a marketplace to enhance the
exploitation of EU funded ICT
models, tools, protocols and policy
briefs related to water and to establish
suitable conditions for new market
opportunities based on these
offerings.
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2.2
Relevant uses cases for a LSP on Smart Farming and Food Safety
AIOTI WG 6 has initially identified a number of domains of application use cases covered by the LSP on smart farming and food safety:
 Raw food production (includes agricultural machinery)
o Plants farming (PF)
 Arable crops (e.g. cereals, potatoes) – (PF-AC)
 Horticulture (e.g. fruits, vegetables) – (PF-HC)
 Permanent crops (e.g. olive, wine) – (PF-PC)
 Organic farming – (PF-OF)
 Conventional farming – (PF-CF)
o Livestock farming (LF)
 Meat production: beef (LF-B)
 Meat production: pork (LF-P)
 Meat production: poultry (LF-P)
 Dairy production (LF-D)
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



 Organic farming – (LF-OF)
 Conventional farming – (LF-CF)
o Other farming options possible, if relevant, such as acquaculture
Food processing (from raw material to food product) – (FP)
Distribution (D)
Wholesale (WS) & Retail (R)
Consumer (C)
Relevant use cases are listed in the table below.
Application case
Water/irrigation
management
Optimization
of
treatments, pest/disease
control
Waste management
Improvement of health
and welfare status in
animals
Monitoring
of
environmental
conditions
through
sensors:
temperature,
humidity,
lightness,
water consumption…)
AIOTI
Relevance criteria
Domain(s) of Economic relevance
application
of the application
domain
(0: not relevant; 5:
most relevant)
Pre-identified
benefits
of
this
application case (e.g.,
operational
efficiency/profitabilit
y/regulatory
compliance / safety…)
PF
PF, LF
PF, LF, FP
LF
PF, LF
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Ecological
impact:
(e.g.,
reduces waste,
carbon footprint,
chemicals)
Geographic
al impact
Involves
several phases
of the "from
(EU regions farm to fork"
potentially
chain
involved)
(If yes, which
ones)
Has
it
already been
tested
(If
yes,
indicate the
project,
initiative,
product…)
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Disease management
Precision feeding
Monitoring
of
production and animal
growing phases
Stock traceability
LF
LF
LF
PF, LF, FP,
D, WS, R
Integrate
relevant FP, D
information
from
providers and customers
to optimize production,
logistics, etc
Provide certification in PF/LF, FP, D,
the products to improve WS, R
the commercial sales
Smart detection of fraud PF/LF, FP, D,
or
substitution
in WS, R
products
Organic certification up PF-OF, LFto the consumer
OF
Smart human nutrition C
(food & health)
Improvement of food WS, R, C
safety in the retail-tofork part of the food
chain, especially at
home
Effective
monitoring PF, LF, FP,
and management of D, WS, R
residue
and
contaminants in the
food/feed chain
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2.3
Recommendations on the feasibility of a LSP covering Smart Farming and Food Safety
Operational recommendations received so far:





The pilots focused on plant farming must be operative for at least one growing cycle and
demonstrate a meaningful benefit on agricultural terms (for instance, optimized irrigation schedules
achieved by the new system).
End-users want to be independent of specific proprietary solutions, so the LSP should include
interoperability (communication layer, data sharing) as a key priority to avoid vendor lock-in, allow
changing service/HW/SW providers.
Data ownership is a key issue. Clear rules/governance of data ownership should be considered to
ensure that the data generated are available for its use by the different stakeholders involved in the
pilot, and can be shared across different pilots/domains.
Education and training aspects (use of new technologies by end-users) should be included in the
LSP.
Your recommendations here.
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3
Investigation of the technological dimension for the large scale pilot [5-6
pages]
Leading editor: Luis Pérez-Freire (Gradiant)
3.1
Mapping of relevant technologies and standards applicable to Smart Farming and
Food Safety
The Internet of Things concept covers and joins a variety of applications, domains and technologies, each
one with their inherent characteristics and specific challenges, Smart Farming and Food Safety being one
of them. Thus, in order to accommodate to their requirements and needs in a scalable and modular way, an
architecture reference model needs to be established. At the moment, several architecture reference models
have been proposed by various initiatives and projects. At the European level, the IoT-A project [IoT-A]
has managed to create a reference architecture with this purpose in mind. The IoT World Forum aims also
to create an architecture reference model to deal with these issues [IoTWFRA]. Standardization
organizations such as the IEEE Standards Association are also working with this objective in mind
[IEEEP2413].
Smart devices from several vendors are being used with sensors, actuators and other appliances (including
those integrated within agriculture machinery) for several purposes in Smart Farming and Food Safety. On
one hand, in the Smart Farming domain, these devices are typically used to gather information from the
fields, animals, and farms, and processed afterwards for creating models, forecasting behaviours or
applying other analytical techniques. Examples of parameters being monitored by sensors can be soil
moisture, leaf wetness or calf temperature. Actuators are used for smart irrigation or automatic feeding and
other appliances such as GNSS devices (GPS, GALILEO) are being used for precision farming and
geolocalization of cattle. On the other hand, in the Food Safety domain, smart devices are being used for
quality monitoring through the value chain (spoilage, break of the cold chain, etc.) or for interacting with
smart labelling.
In order to communicate these smart devices several approaches can be found:
Regarding Smart Farming applications, wireless technologies are typically used, especially those with low
power consumption rates. Hence, battery powered nodes are the preferred option due to the difficulty and
worthlessness, both in cost and work efforts, of complete wiring a deployment. Several technologies are
being used, many of them coming from the Wireless Sensor Networks (WSN) field. ZigBee is one of the
main technologies when deploying mesh networks although 6LoWPAN is starting to gain momentum too.
Other approaches follow cellular networking using several radio interfaces like Sigfox or 3G/GPRS
modules. Nonetheless, both cases need stations connected by wire to another network, acting as gateways
or bridges to send data to online platforms or to the cloud.
Tractors and agricultural machinery are not an exception and they are currently equipped with several
monitoring capabilities. CAN Bus J1939 [J1939] or ISOBUS [ISO11783] standards are used in this case
for transferring information between them or inside their own systems.
Concerning Food Safety and Traceability applications, the smart technologies being used are mainly
oriented to tracking purposes. Using RFID or NFC tags attached to food products it is possible to know the
stages it followed through the value chain registering this information in platforms for further processing.
Image or thermal sensors are also used for monitoring product status.
Once data is retrieved from smart devices in the edge of the networks, it is managed, stored and further
processed for visualization or other type of operations with the help of several platforms or cloud services.
FIWARE [FIWARE] is a platform created through European public-private collaboration aiming to grant
interoperability independently from the underlying protocols or standards used while contributing other
tools with analytical, visualization, storage and many other purposes. SOFIA2 [SOFIA2] is also a similar
platform with akin objectives. There are also many platforms from private vendors such as Cisco or
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Thingworx.
References for this section
[IoT-A] FP7 project IoT-A, online, http://www.iot-a.eu/
[IoTWFRA]
IoT
World
Forum
Reference
Architecture
presentation,
online,
https://www.iotwf.com/resources/72
[IEEEP2413] IEEE Standards Association, Standard for an Architectural Framework for the Internet of
Things, Online, https://standards.ieee.org/develop/project/2413.html
[J1939] CAN Bus J1939 standard, online, http://www.saedigitallibrary.org/corporate/smallbusiness/j1939/
[ISO11783]
ISO11783
standard,
online,
http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=54390
[FIWARE] FIWARE webpage, online, https://www.fiware.org/
[SOFIA2] SOFIA2 webpage, online, http://sofia2.com/
Technology
levels
Available technologies, architectures, standards
Technologies
Architectures
Standards
Enabling
hardware
Enabling
software
Enabling
networks
Platform
Services
3.2
Requirements for the selection of technologies, standards, and interoperability for the
LSP
The IoT architecture model must be flexible enough to properly cover requirements from territories with
dissimilar characteristics such as areas where smallholdings are the main agriculture model (e.g. Northern
Spain) or areas where larger fields are harvested (e.g. Germany). Typically, in the former case the
concentration of users and feeds of information will be higher for a given place. Hence, shorter range radio
technologies can be used and proper communications planification and scheduling procedures and privacy
preservation measures should be followed. However, in the latter case the architecture model will need to
deal with higher distance transmissions so higher range radio technologies may be needed to cover a field.
The IoT architecture model used by the pilot should also ensure compatibility with current deployments
made by farmers in their fields or inside their barns. Farmers should not have to completely change their
infrastructures with new equipments. Nonetheless, wherever new deployments have to be made, open
hardware and software solutions common to everybody should be used. For both cases, interoperability can
be achieved at the data level, creating common APIs that are independent of the underlying protocols.
Systems and smart devices being used for Smart Farming activities should have proper mechanisms to ease
their deployment and allow to dynamically add more nodes. Thus, new use cases can be exploited with
little effort. Standard interfaces and APIs, self-configuring methodologies, semantic interaction and other
methodologies will help to achieve this objective.
In the past, precision farming applications focused on using data recovered from fields for improving
economic revenues and farm attributes with little or none social implications. The wide amount of data
generated by IoT devices, deployed in crop fields or in animals, shall be used when possible by the
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community. Therefore, proactive measures may be taken if pests, plagues or animal diseases are detected,
preventing their spread. Open data models and platforms should be used for this purpose while maintaining
privacy measures and protecting farmers’ interests.
Common semantic models must be used by all actors. An existing one must be adopted or a new one created
if it does not exist.
Food Safety and Traceability:
The food industry requires traceability systems to ensure a higher level of granularity through the value
chain and inside each of the stages where products are processed.
Interoperability between actors in the value chain needs to be achieved. A common traceability framework
should be used by all.
Integrity of the data must be ensured by all means. The origin of the product, the stages it passed through
and other sensitive information must be known. Guarantee the trustworthiness of the source.
A balance between data that may have to remain private and the share of open information must be
achieved. Customers will be grateful to know the product is in good shape but they may not need to know
sensitive information from companies that may need to be incorporated for self-traceability purposes. Thus,
an access control policy must be established.
Technology
levels
Selection criteria
Requirements
Technology
KPIs
TRL in / Do previous tests /
TRL out
implementations
exist?
Enabling
hardware
Enabling
software
Enabling
networks
Platform
Services
3.3
Recommendation on the feasibility and replicability of the LSP
Feasibility
 Smart Farming
o The chosen technology for communications with nodes deployed in fields or animals will
need to be based on wireless standards. Wired deployments are too costly and not useful
for mobility use cases.
o When using wireless communications, range and network coverage must be taken into
account.
o Low power technologies should be used. Battery powered devices should have power for
years when deployed in a field in order to diminish costs. Strategies for recharging those
batteries must be contemplated (solar power, energy harvesting techniques) to increase
device life.
 Food safety and traceability
o Traditionally, tracking by using RFID or NFC tags has been problematic due to the cost
associated with this technology compared with the cost of the product. Recommendation?
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General:
 Data ownership is a key issue. Clear rules/governance of data ownership should be considered to
ensure that data is available for its use by the different stakeholders taking part in the LSP.
Replicability
 General
o Standard interfaces between the different levels of the IoT ecosystem should be used. This
will allow to replicate situations and architectures easily in different conditions.
o APIs at the highest level should be clearly defined. High level applications need a stable
way to access lower level information.
o For open data sharing, standard management systems like CKAN should be used.
 Smart Farming
o Historical data from past problems with plagues or pests should be shared. The conditions
that caused a problem in a given place may prove to be useful to avoid the same situation
in another site.
o Pilots should be deployed in regions with different agricultural conditions (soil moisture,
temperature, soil composition, types of cultivated crops, etc.). This will allow to test that
the technologies, architectures or devices are valid for a variety of conditions.
 Food safety and traceability
o Methodologies used for food traceability should be valid for every type of product,
independently from their inherent characteristics.
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4
Recommendations for the testing of business models and of user
acceptability [5-6 pages]
Leading editor: Lyse Brillouet (Orange)
See Appendix 1
4.1
Recommendations for the testing of business models
Important elements underlying any business model
 Security is seen as a key element for building trust among the different business stakeholders
involved in any application case.
 Legal compliance and liability across all business actors involved in one final single
product/service.
 Your recommendations here.
Examples of business models currenty in use
 Your examples here.
Methodology for the testing of business models
 Your recommendations on methodology here.
4.2
Recommendations for the testing of user acceptability
General recommendations
 Security and privacy must be properly taken care of as key elements to build trust in the end-user.
 Your recommendations here.
Involving end-users and testing the pilots with them
 Your recommendations here.
Testing and evaluation of users’ acceptance
 Your recommendations here.
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5
Investigation of the operational dimension for the large scale pilot [2-3
pages]
Leading editor: Luis Pérez-Freire (Gradiant)
It is expected that the LSP will bring together a large number of entities coming from a wide variety of
domains both on the IoT supply side (hardware manufacturers, telecom operators, and in the IoT demand
side (farmers, machinery manufacturers, food processing plants, distribution and retail companies, and
cosumers, to name a few), as well as research technology organisations and universities, which could may
belong to either side.
It is also expected that the LSP would integrate a number of individual pilots, dealing with particular
application cases, possibly based in different locations across Europe, and involving (not necessarily only)
local partners.
Recommendations:
 The LSP should involve a comprehensive representation of stakeholders both from the IoT supply
and demand side.
 [Use-case coverage] The LSP should cover a meaningful number of application cases that are
relevant for the farming and/or food safety domains in Europe.
 [Geographical coverage] The LSP should guarantee a wide geographical coverage across Europe.
 [Validation] The LSP must be tested during a significant period of time in order to demonstrate
meaningful benefits and its adaptability for replication.
 The LSP should include specific and realistic quantified indicators to monitor progress at different
stages during the implementation
 In the case of smart farming, LSP should include non-intrusive IoT technologies or demonstrate
they do not affect to animal life.
5.1
Governance of the consortium
Different dimensions of the governance:
1) Pilot-wise organisation: two-level governance
 A governance body at full LSP level, in charge of monitoring the implementation of the individual
pilots, among other possible responsibilities.
 A governance body at the level of each individual pilot, in charge of managing the pilot, able to
represent the individual pilot and the partners involved in it and liable before the LSP governance
body.
2) IoT Supply-Demand governance. To facilitate the exchanges between both sides, this body should
integrate a representation of the full consortium, and coordinate some of the cross-cutting activities dealt
within the LSP, collection of demand-side requirements, training activities, business models, end-user
acceptability.
Coordination/interaction among LSPs
It is expected that a number of parallel LSPs will be funded through the next H2020 ICT call under the IoT
Focus Area, each covering different vertical market domains. The LSP should be prepared to share
information and cooperate with other LSP, in particular to define and adopt a common infrastructure
methodology The LSP on Smart Farming and Food Safety should allocate the necessary resources to allow
for a proper interface with the rest of LSPs, and the CSA supporting the implementation of the LSPs.
The purpose of such interactions is manifold: benchmarking and mapping of the pilots and the technologies
implemented, inputs for policy-making, awareness, identification of success stories, etc.
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5.2
Facilitating collaboration
It is expected that the heterogeneous nature of all the actors involved in the LSP may pose some difficulties
when it comes to collaboration. The LSP should take the necessary measures to break such barriers. (One
possible solution is the design of a proper governance such as the one described above).
In order to reduce collaboration barriers the LSP should contribute to the improvement of the multi-check
points over the value chains ensuring reliability in the whole process and thus between parties involved.
Clear rules regarding data usage and data ownership should be defined to prevent abusive behaviors from
stakeholders, and thus favor adoption of IoT solutions in smart farming & food safety.
[Should we identify already some barriers here?]
 Your identified barriers here.
Intellectual Property
(To be developed)
 Your recommendations here.
5.3
Sustainability of the pilot beyond the funding period
One key element to the sustainability of the solutions implemented in the LSP is the identification of
proper business viability conditions. The validation phase should provide socio-economic evidence for
ICT investments in the field, including return of investment and user acceptance. Recommendation: LSP
should include detailed plans for sustainability after the LSP funded period.
Synergies with other (co)funding sources should be seen a strength whenever it can be proven that there is
no overlap but actual complementarities. This applies for example to Structural Funds such as the EAFRD,
ERDF or interregional funds, typically managed at national or regional levels.
Synergies with related initiatives or programmes expected to survive the LSP should be seen as a strength.
5.4
From the pilot to the market
The LSP approach should be clearly demand-driven, ensuring acceptance and uptake, involving end-users
during the whole duration of the project in order to accelerate market acceptance and wide deployment of
innovative ICT systems in Europe after the LSP execution. Results obtained from the LSP must be
focused on leading to substantial savings and easy deployment which impulse its market acceptance.
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6
Next steps [1 page]
The adoption of IoT for smart farming will greatly benefit from proper awareness actions, which could
encompass additionally training or education activities as the market of technologies and services starts
gaining momentum.
AIOTI WG6 foresees to perform awareness efforts starting from Q4 2015 targeting the end-user community
(farming sector, food processors, etc.). Such awareness raising actions should possibly be co-located with
already existing events that gather the target community.
Possible events for dissemination may be found here (to be decided, suggestions welcome):



7
Meetings EIP-AGRI Focus Group Precision Farming:
https://ec.europa.eu/eip/agriculture/en/content/seminars
General events oriented to the Farming Sector:
https://ec.europa.eu/eip/agriculture/en/news-events/events/european-calendar
Events listed in the ERA-NET ICT Agri website:
http://www.ict-agri.eu/events
References
(Formatting example)
[1]
B.G. Buchanan and E.H. Shortlife. Rule-Based Expert Systems: The MYCIN Experiments of the
Stanford Heuristic Programming Project. Addison-Wesley Publishing Company, 1984.
[2] S.M. Carberry. Modeling the user’s plans and goals. Computational Linguistics, 14(3):23–27, 1988.
[3] H.C. Bunt. Modular incremental modelling of belief and intention. In Proceedings of the Second
International Workshop on User Modeling, 1990.
[4] W. Strunk and E.B. White. The Elements of Style. Turtleback Books, 1999.
[5] B. Cahour. La modélisation de l’interlocuteur: Elaboration du modèleeteffets au cours de dialogues
de consultation. PhD Thesis Université Paris, France, 1991.
[6] B. Cahour. Competence modelling in consultation dialogs. In L. Berlinguet and D. Berthelette (Eds.),
Proceedings of the International Congress, Work with Display Units 89, Amsterdam, 1990.
[7] B. Carr and I. Goldstein. Overlays: A theory of modelling for computed aided instruction. AI Memo
406, 1977.
[8] J. R. Carbonell. AI in CAI: An artificial intelligence approach to computer-aided instruction. IEEE
Transactions on Man-Machine Systems, 11:190–202, 1970.
[9] J.S. Brown and R.R. Burton. Diagnostic models for procedural bugs in basic mathematical skills.
Cognitive Science, 2(2):155–192, 1978.
[10] A. Cawsey. Planning interactive explanations. International Journal of Man-Machine Studies, in
press.
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Appendix 1 - Questionnaire for knowledge gathering on business models
Who you are
Name:
7.1.1.1
Affiliation:
7.1.1.2
Your role:
Contact details (email, phone):
Website:
7.1.1.3
Your background
1. Why are you interested in contributing to AIOTI – WG 6?
2. What can you bring to AIOTI – WG 6?
3. What specific experience / know-how do you have regarding business models (testing) and/or enduser acceptability (testing) in regard to Smart Farming and Food Safety? (Example: experience with
actual products or services already in the market, involvement in pilots, etc.)
Regarding the testing of business models
The market segment of IoT-based devices, solutions and services for Smart Farming and Food Safety is
very incipient. Whereas there are already some companies offering for example some smart farming
solutions based on connected components, or including such components in their products, it is expected
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that the size of this market should increase dramatically over the next 5-7 years.
Within this market, there will be probably a mixture of business models corresponding to the large
number of business stakeholders that we somehow try to represent in the diagram above. The future
Large Scale Pilot in Smart Farming and Food Safety should take care of testing and validating some of
these business models.
The questions below try to collect initial inputs from the AIOTI WG6 Community on this regard, and will
be used to complete the corresponding section of the AIOTI WG6 Recommendations Report.
1. As a company, are you currently selling IoT solutions and/or IoT products (sensors…) to the Smart
Farming and/or Food Safety sector? If so, please indicate your positioning in the diagram above
and briefly explain your business model.
2. The Smart Farming sector is highly fragmented. How do you think this must be taken into account
for the definition of a successful business model?
3. Closed systems (possibly vertically-oriented) vs. open models (open APIs, data sharing, etc): under
your point of view, what are the pros and cons of each approach?
4. Can you foresee any particular “not-yet-existing” business model for IoT-based products and
services applied to Smart Farming and Food Safety that would be successful in your opinion?
5. How do you suggest that business model should be tested in a future pilot?
Please list the benchmark criteria that, under your point of view, should be taken into account for
evaluating the success of the business model (e.g. monetization of the solutions to be tested).
Please provide indications on how to test each of them.
Regarding the testing of end-user acceptability
1.
According to your opinion, what will be the key factors / drivers that will make the difference
in terms of end-user acceptability of the future IoT-based products, services and solutions for
Smart Farming and Food Safety?
Please indicate, for each factor:
(1) what end-user you have in mind (e.g., potato farmer, meat processing plant, distributor,
wholesale, consumer…)
(2) what factor(s) / driver(s)
(3) why that factor is relevant
2.
What barriers can you foresee in terms of end-user acceptability?
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3.
AIOTI
Do you have any specific recommendation on how end-user acceptability should be tested in
a future pilot?
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Editors:
Luis Pérez-Freire. Gradiant, Spain; Lyse Brillouet, Orange, France.
Contributing Companies, Projects, Initiatives, SDOs:
Name1, Name2,
Additional Contributing Experts:
First Nam1e Family Name1, Company, Country, First Name2 Family Name2, Company, Country
Acknowledgements
The AIOTI would like to thank the European Commission services for their support in the planning and
preparation of this document. The recommendations and opinions expressed in this document do not necessarily
represent those of the European Commission. The views expressed herein do not commit the European
Commission in any way.
© European Communities, 2015. Reproduction authorised for non-commercial purposes provided the source is
acknowledge.
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