This is a keynote presentation presented at a conference on INNOVATIVE TECHNOLOGIES AND DATA APPLICATIONS IN THE AGRIFOOD SECTOR, 26 February 2019 at Boğaziçi Üniversitesi South Campus, Rectorate Conference Hall, Turkey. It describes multi-disciplinary, collaborative, agile approach for digital transformation of the agri-food sector based on the IoF2020 and SmartAgriHubs project. It describes several examples of IoT and Big Data applications from those projects, The video and voice-over of this presentation can be found at https://youtu.be/wYJVqh6jvSE

1. IoT and Big Data Applications in Agri-Food
Dr. Sjaak Wolfert
Strategic Senior Scientist at Wageningen University & Research
Boğaziçi University, Istanbul, Turkey, 26 Feb. 2019

2. Wageningen University & Research: Two Partners
Wageningen University & Wageningen Research

3. To explore the potential of nature
to improve the quality of life
Wageningen University & Research
 Academic research & education, and applied research
 5,800 employees (5,100 fte)
 >10,000 students (>125 countries)
 > 30 locations in NL, also global satellites
 Turnover about € 650 million
#1 Agricultural Sciences
14x

4. 4
Interview with
Johan Bouma in
Resource 4 Oct. 2018
p. 18-19
1. Multidisciplinarity
2. Collaborative process
3. Agile development

5. ECOSYSTEM & COLLABORATION SPACE
ProjectCoordination&
Management
Take-home message: multidisciplinary, collaborative,
agile approach for Digital Transformation
Trials/Use Cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
Data Science &
Information management
Business Modelling,
Governance & Ethics
Ecosystem Development

6. BIG
DATA
Smart Farming: context-aware system
CONTROL
SENSING
& MONITORING
ANALYSIS
& PLANNING
SMART
SMART
SMART

7. Involving entire supply chain and beyond
Smart Farming
Smart Logistics
Tracking & Tracing
Consumer trends
Domotics Health
Fitness/Well-beingPersonalized

8. Big
Data
Analytics
Internet
of Things
Blockchain
Technology
Linked
Data
Cloud
Computing
Artificial
IntelligenceDATA
SENSING
& MONITORING
ANALYSIS
& PLANNING
SMART
SMART
SMART
Public Decision-Making
food safety food security
healthenvironment
nutrition climate
CONTROL
Corporate Decision-Making
& Food Integrity
The Digital Transformation of Agri-Food

9. The Battlefield of Data for Farming and Food
Farming
Data
Food
Data
See: Wolfert et al., Agricultural Systems 153 (2017) 69–80
Processors
Ag
Business Tech
Companies
Tech
Start-up
Tech
Start-up
Ag Tech
Retail
Venture
Capitalists
Data
Start-up
Data
Start-up

10. Governance and business model issues
10
Code of Conduct

11.  Governance
● Trust, data privacy, security...
 Business models
● fair share, new opportunities
 Infrastructure
● open versus closed
 Ecosystems
● establishing critical mass
...which are often intertwined!
Current issues and challenges

12. European Public-Private Partnership ICT-projects
2011-2013: SmartAgriFood - a FIWARE-based conceptual architecture
and prototype applications (5 M€)
2013-2015: FIspace – B2B business collaboration platform for agri-food
& logistics (+ apps) (13.5 M€)
2014-2016: Accelerators: SmartAgriFood2, FInish, FRACTALS (~17 M€)
- 125 apps/start-ups based on FIWARE/FIspace
Sep. 2016: FIWARE Foundation established with 3 verticals:
Smart Cities, Industry and Agri-Food
2017-2020: IoF2020 – The Internet of Food and Farm (30 M€) - IoT
large-scale pilot for smart farming and food security
2018-2022: SmartAgriHubs – Connecting the dots to unleash the
innovation potential for digital transformation of the
European Agri-Food sector (20 M€)

14. Objective:
Large-scale uptake of IoT in the European
farming and food sector
• Business case of IoT
• Integrate and reuse available IoT
technologies
• User acceptability of IoT
• Sustainability of IoT solutions
14
Internet of Food and Farm 2020
Innovation Action: 2017 - 2020
30 M€ funding by DG-CNCT/AGRI

16. THE INTERNET OF ARABLE FARMING
1.1 Within-field Management Zoning (potato)
1.2 Precision Crop Management (wheat)
1.3 Soya Protein Management (soya)
1.4 Farm Machine Interoperability
- Data-Driven Potato Production
- Solar-powered Field Sensors
- Within-field management zoning (Baltics)
- Traceability for Food and Feed
- Potato Data processing exchange

17. THE INTERNET OF DAIRY FARMING
17
2.1 Grazing Cow Monitor
2.2 Happy Cow
2.3 Silent Herdsman
2.4 Remote Milk Quality
- Precision Mineral supplementation
- Lameness detection through machine learning

18. 3.1 Fresh Table Grapes Chain
3.2 Big Wine Optimization
3.3 Automated Olive Chain
3.4 Intelligent Fruit Logistics
THE INTERNET OF FRUIT
18
- Smart Orchard spray application
- Beverage integrity tracking

19. 4.1 City Farming for Leafy Vegetables
4.2 Chain-integrated Greenhouse Production
4.3 Added Value Weeding Data
4.4 Enhanced Quality Certification System
THE INTERNET OF VEGETABLES
19
- Digital Ecosystem Utilisation

20. 5.1 Pig Farm Management
5.2 Poultry Chain Management
5.3 Meat Transparency and Traceability
THE INTERNET OF MEAT
20
• Feed Supply Chain management
• Interoperable Pig health tracking
• Decision-making optimisation
Beef supply chain

21. Soil map based variable rate applications and machine automation in potato production
UC1.1. WITHIN-FIELD
MANAGEMENT ZONING
Coordinators: Peter Paree (ZLTO) & Corné Kempenaar (WUR)

22. SOIL MAP SERVICE
VARIABLE RATE
APPLICATION MAP
AUTOMATION & MACHINE
COMMUNICATION
Product Impressions

23. Major Challenge Here is what we aim to improve (KPIs)
Yield by better
plant distribution
Variable planting distance map –
Validation in 2017 and 2018. Nov. 2018
portal where maps can be ordered.
Variable rate herbicide use map -
Validation in 2016 and 2017. May 2018
portal where maps can be ordered.
Quality by better
plant distribution
Reduction
pesticide use
Core Product Features
Variable Rate
Application Map Service
Customer & Provider
Uses soil maps and agronomic knowledge to create
crop management task map based on variability in
soil characteristics like organic matter and/or clay
content, water storage capacity, tramlines, shade,
etc..
Smart application of resources: seeds,
pesticides, fertilizers +4%
+5%
-23%
Better distribution of plants leads to +5% kilos and +5% better
quality (more potatoes in desired size). Taking soil characteristics
for weed growth into account: -23% less herbicide and +2% more
yield.
Enriching canopy index with soil characteristics lead to -10% less
additional N fertilizer (2nd phase).
These values derive from comparison of a standard farm’s performance
prior to the installation of our system and after.
Reduction
fertilizer use
-10%
Product Factsheet
Existing variable rate maps are often based on tweaking
expert judgement and lack a certain level of precision in
tasking / lack of validation.
Farmers and advisors
Price per unit, added value
LoonwerkGPS,
soil analysis labs,
FMIS providers VRA additional N spraying
June 2018 on Growth + Soil Maps.
High spatio-temporal monitoring dashboard

24. IoT tools for sustainable wine production, wine quality management and shipping monitoring
UC3.2. BIG WINE
OPTIMIZATION
Coordinators: Mario Diaz Nava, ST Microelectronics

25. Multi-actor approach
JANUARY 1 2017

26. IoT Product Impressions
sensors in
the vineyard
display devices,
agronomic parameters
and weather forecast
Temperature/RH
logger
with data
transmission
NIR spectrometer
% alc., sugar,
etc.

27. PART OF THE BUSINESS MODEL
‘PROCESS2WINE’

28. Keep your herd healthy with an artificial intelligence monitoring system
UC2.2.
HAPPY COW
Coordinators: Niels Molenaar, Connecterra

29. Estrus insights Health insights Value chain integration
Product Impressions
How IDA looks like in practice

30. Translating dairy cow behaviour to management
information that helps a farmer to improve farm
efficiency and animal health.
Major Challenge
Here is what we aim to improve (KPIs)
Calving interval
305 day milk
production
Number of
days treated
with antibiotics
Customer & Provider
Ida a ‘farmers assistant’ based
on artificial intelligence; it helps
the farmer to keep the herd
healthy.
Dairy farmers
-3%
+1%
-0.5%7,5
€/cow/month
Detect oestrus with 80% accuracy
Detection of health problems.
Predict the start of calving
Rank cows based on their feed efficiency
Track management problems based on herd
behaviour.
Insights on dairy herds for partners like
veterinarians and dairy processors with.
Core Product Features
Tracks cow behaviour and learns from the
observed patterns to advise the farmer.
Product Factsheet
IDA: the Intelligent Dairy farmers Assistant

31. IOF2020 ECOSYSTEM & COLLABORATION SPACE
WP1ProjectCoordination&
Management
GENERIC APPROACH & STRUCTURE
WP2 Trials/Use cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
WP3 IoT Integration WP4 Business Support
WP5 Ecosystem Development

32. TECHNICAL / ARCHITECTURAL APPROACH
Use case
architecture
Use case
IoT system
developed
Use case IoT
system
implemented
Use case IoT
system
deployed
USE CASE REQUIREMENTS
IoT reference
architecture
instance of
IoT catalogue
Reusable IoT
components
reuse
IoT Lab
Reference
configurations
& instances
reuse
Collaboration
Space
shared
services
& data
ProjectlevelUsecaselevel
sustain
reuse

33. www.iot-catalogue.com
FARMER TECHNOLOGY
PROVIDER

34. Business support
Different business
models will be
tested to identify
the most successful
and sustaining ones
BUSINESS MODELS
Buying and selling a
product is te best
service.
MARKET
STUDY
Develop standard
procedures and
guidelines to handle
sensitive
information and to
protect IP
PRIVACY
GUIDELINES
Calculate costs
savings and effects
on revenue
development &
financing plans for
farmers
KPI & IMPACT

35. ECOSYSTEM & COLLABORATION SPACE
ProjectCoordination&
Management
Multidisciplinary, collaborative, agile approach for
Digital Transformation
Trials/Use Cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
Data Science &
Information management
Business Modelling,
Governance & Ethics
Ecosystem Development

36. 36
SmartAgriHubs
:
Where IoF2020 stops and
the Digital Agri-Food
Innovation continues...
Establish EU-wide network
of Digital Innovation Hubs
for Digital Transformation
of Agriculture
• Build network covering all EU
regions including technology,
business, sector expertise
+ relevant players
• Critical mass of multi-actor
Innovation Experiments
• Financial support 3rd parties
by open calls - various
public/private funds
• Ensure long-term sustainability
incl. business plans
+ attracting investors
• Promote DIH’s full innovation
accelerating potential

37. 37
Digital Innovation Hub
Incubators
Government
Cooperatives
Farmer communities
Investors
Others
Advisories
Research organisations
Start-ups
Education & training institutes
Large companies
Industry associationsCompetence Center
Other Competence
Centers
Orchestrator
Other
DIHs
Innovation
Experiments

38. 38
DIH innovation services
Ecosystem
• Community building
• Strategy development
• Ecosystem learning
• Project development
• Lobbying
Technology
• Strategic RDI
• Contract research
• Technical support on scale-up
• Provision of technology
infrastructure
• Testing and validation
Business
• Incubator/accelerator
support
• Access to finance
• Skills and education

39. 39
5 basic concepts of SmartAgriHubs to build a EU-wide ecosystem
Innovation
Portal
Innovation
Experiments
Digital Innovation
Hubs
Innovation service
maturity model for
DIHsCompetence
Centres
Layered network
of CCs & DIHs in
Regional Clusters

40. 40
SmartAgriHubs in numbers (20M€)
Ecosystem
108 Partners
Involved covering all EU
68 partners are SMEs
54% of budget allocated to SMEs
Digital
Innovation
hubs
140 DIHs in the existing Network
covering all 28 Member States
Regional Approach
9 Regional Clusters
Attract 260 New DIHs
Flagship
innovation
experiments
28 FIEs
22 Countries involved
13 Cross-border collaboration FIEs
(47%)
Impact
30M additional funding
Mobilized from other sources(public,
regional, national and private)
80 new digital solutions
Introduced into the market
2M Farms involved in digitisation
Open Calls
6M Euros distributed through
Open Calls
75% Open Call budget to SMEs
70 New Innovation Experiments
Arable 8
(28,6)
Fruit 4
(14,2%)Vegetables 5
(17,8%)
Livestock 10
(35,7%)
Aquaculture 1
(3,5%)
5
sectors

41.  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
 Digital Innovation requires a multi-disciplinary, collaborative, agile
approach
● Governance and Business Models are key issues
● There is a need for a facilitating open network infrastructure
Conclusions
41

42. Thank you for your
attention!
More information:
sjaak.wolfert@wur.nl
nl.linkedin.com/in/sjaakwolfert/
Twitter: @sjaakwolfert
http://www.slideshare.net/SjaakWolfert
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