Presentation for Alumni of the Van Hall Larenstein University of Applied Science.

1. The Internet of Food and Farm
Sjaak Wolfert
Sr. Scientist Information management
& ICT in Agri-Food
Alumni-bijeenkomst Precisielandbouw, HVHL, Leeuwarden, 6 December 2016

2. Our mission and approach
Support the agri-food business in implementing ICT
solutions by:
 Analysis – what are the ICT challenges for your
business/sector?
 Design – how should the ICT-solution look like? (based
on reference architecture/infrastructure)
 Iterative implementation – by developing pilots and
prototypes, mostly in sector-wide or beyond-sector
public-private projects

3. everything
anything
everybody
ICT as a driver for innovation
Mobile/Cloud Computing
Location-based monitoring
Social media
Internet of Things
Blockchains (?) =
Big Data = The New Oil?
anywhere
We ain’t see nothing yet!
everywhere

4. How will ICT drive innovations in agri-food?
Transport
Current issues
How more ICT and data can contribute to this
Which innovations and new business concepts are possible ?
Major global challenges
Transpo
rt
Transport
Input industry Farmer Food Processor Retail / consumerSoftware Provider Logistic Solution Provider

5. Current issues
Transport Transpo
rt
Transport
Input industry Farmer Food Processor Retail / consumerSoftware Provider Logistic Solution Provider
High-Tech
Interoperability
Cost price &
Quality
New services
Consumer demands
Loyalty
On-line shopping

6. Major global challenges
Transport Transpo
rt
Transport
Input industry Farmer Food Processor Retail / consumerSoftware Provider Logistic Solution Provider
Food & Nutrition Security
Sustainability
Health
Food Safety & Transparency

7. How can ICT add value?

8. FI-Ware enabled
Cloud Platform
Cloud
Information
systems
SmartAgriFood’s conceptual cloud architecture
sensors
actuators
data sources
(‘Internet of Things’)
local
Information systems
App store
Services
Spraying Advisory
Services
Meteorological
Service
State and Policy
Information Service
Consumer Food
safety service
E-agriculturist Service
for spraying potatoes
Machine Breakdown
Service
Transport
User’s
devices
Other
sources

9. smart sensing
& monitoring
smart analysis
& planning
smart control
Smart Farming: closing the cyber-physical
management cycle
BIG
DATA

10. Ag Equipment: mobile networks
• Tractor and Implement are acting as one network
• Always connected!
Cloud / Internet

11. PAGE11
WWW.CONNECTERRA.IO
Our solution: The Dairy Monitor
Complete animal healthcare in one device.
Based on sensor data we are able to provide the farmer with valuable insights and actions
Heat and Health Detection
Farmer Insights
Location Services
No more graphs to understand,
the system tells you what to do!
Track animal movements and
grazing habits to enable
organic certifications
Early detection of heat and
health issues improves
productivity by 20%
Sensors
Algorithms run
in the cloud
Insights &
actions

12. Involving the entire supply chain network
and beyond
Smart Farming
Smart Logistics
tracking/& tracing
Source: Hisense.com
Domotics Health
Fitness/Well-being

13. Cloud Event Management System
Location A Location B
Virtual
Plant
Virtual
Location A
Virtual
Location B
Environ
ment
update
Plant
location
update
Environ
ment
update

14. Tracking and Tracing and Meat Awareness (TTAM)

15. What needs to happen behind the scenes...

16. Smart Food Awareness
To satisfy needs of each consumer by
providing transparent and tailored
information about agri-food products.
I am a Royal Gala apple
from south Spain, I was
grown without
pesticides following
organic farming criteria,
I have been here for 1
day, my carbon footprint
is 1,2 kg CO2e.
I am a self-
conscious
consumer that
wants to now
where my food
comes from and
how it is produced

17. App store
From conceptual architecture & prototypes to a real software platform
and Apps
Services
sensors
actuators
data sources
(‘Internet of Things’)
Local
ISs
Spraying Advisory
Services
Meteorological
Service
State and Policy
Information Service
Consumer Food
safety service
E-agriculturist Service
for spraying potatoes
FI-Ware enabled
Cloud Platform
Machine Breakdown
Service
User’s
devices
Other
sources
Cloud
IS
Transport
I2ND
IoT
IoC
IoS
S&T
GENERIC ENABLERS
Base Technologies
Validation
T270: Security, Privacy, Trust Framework: SPT (KOC)
T250: System & Data Integration
(ATOS)
T240: B2B Collaboration Core
(IBM)
T230: App Store (IBM)
T220: User Front-End (ATOS)
T260:OperatingEnvironment
(IBM)
T280:SoftwareDevelopmentToolkit:
SDK(ATOS)

18. FIspace platform High Level Architecture
I2ND
IoT
IoC
IoS
S&T
GENERIC ENABLERS
Base Technologies
Validation
1. Crop Protection
Information Sharing
2. Greenhouse
Management &
Control
3. Fish Distribution &
(Re-)Planning
4. Fresh Fruit and
Vegetables QA
5. Flowers & Plants SC
Chain Monitoring
6. Meat Information
Provenance
7. Import & Export of
Consumer Goods
8. Tailored Information
for Consumers
Trials:
Security, Privacy, Trust Framework: SPT
System & Data Integration
B2B Collaboration Core
App Store
User Front-EndOperatingEnvironment
SoftwareDevelopmentToolkit:SDK

19. Example: Spraying Scenario
Scenario: get expert advice for spraying to handle disease on tomatoes
State AuthorityFranz Farmer Ed Expert
Spraying
(follow advice)
Create
Advice
Approval
Request
Advice
CollaborativeBusinessProcessBack-EndSystems
Farm / GH
Management
Systems
Sensor Network
in the Greenhouse
Agronomist
Expert System
Regulations &
Approval System
1
2
3
FIspace App
‘Weather
Information’
FIspace App
‘Spraying Expert
Advice’
FIspace App
‘Spraying
Certification’
product type, etc.
sensor data
(access details)
suggested
chemical
advice details
certification
details
19

20. Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
20
3. Smart, connected product
+
+
+
2. Smart Product
1. Product

21. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
21
5. System of systems
farm
management
system
farm
equipment
system
weather
data
system
irrigation
system
seed
optimizing
system
field
sensors
irrigation
nodes
irrigation
application
seed
optimization
application
farm
performance
database
seed
database
weather data
application
weather
forecasts
weather
maps
rain, humidity,
temperature sensors
farm
equipment
system
planters
tillers
combine
harvesters
4. Product system

22. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
22
5. System of systems
farm
management
system
farm
equipment
system
weather
data
system
irrigation
system
seed
optimizing
system
field
sensors
irrigation
nodes
irrigation
application
seed
optimization
application
farm
performance
database
seed
database
weather data
application
weather
forecasts
weather
maps
rain, humidity,
temperature sensors
farm
equipment
system
planters
tillers
combine
harvesters
4. Product system
Your company

23. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
23
5. System of systems
farm
management
system
farm
equipment
system
weather
data
system
irrigation
system
seed
optimizing
system
field
sensors
irrigation
nodes
irrigation
application
seed
optimization
application
farm
performance
database
seed
database
weather data
application
weather
forecasts
weather
maps
rain, humidity,
temperature sensors
farm
equipment
system
planters
tillers
combine
harvesters
4. Product system
Your company
Farmer: how many
platforms must I use?
Developer: on how many
platforms should I offer
my solution?
Platform owner: how
many connections do I
need to maintain?

24. Battlefield of IoT, Big Data and Farming
Farm
Farm
Farm
Farm
Data
Start-ups
Farming
Cooperatives
Open Ag Data
Alliance
...
AgBusiness
Monsanto
Cargill
Dupont
...
Tech
Companies
Google
IBM
Oracle
...
Ag Tech
John Deere
Trimble
Precision planting
...
Tech
Start-upsFarm
Tech
Start-ups
Data
Start-upsVenture
Capital
Anterra
Founders Fund
Kleiner Perkins
...
Farm

25. USA start-ups in different areas

26. Discussions
 Am I owning my own
tractor? (IPR on software)?
 Do I own my data? Who
has access?
 Does the government have
insight?
 Do certain companies get
much power in the market?
 Is there a lock-in situation?
Can I transport my data?
 Do I become a franchiser
carrying the risks and limited
returns?
Code of Conduct

27. Towards business/software ecosystems

28. New Business Models based on Big Data
See: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into profit“, 2014
 Basic data sales
● commercial equivalent of open data (e.g. FarmMobile)
 Product innovation
● use data to improve your product (machinery industry, e.g. John
Deere, Lely’s milking robots)
 Commodity swap
● data for data (e.g. between farmers and (food) processors to
increase service component)
 Value net creation
● pool data from the same consumer (e.g. AgriPlace)
 Value chain integration
● use data to control the whole chain (e.g. Monsanto’s Fieldscript)

29. Commodity swap -
data for data
Many farmsIndividual farm
Data
Benchmark
Analytics

30. Basic data sales
“Farmers think their trust is violated”
Their data goes to multinationals that promise
high future yields based on big data,
while farmers have to pay for everything
How does it work?
- A ‘box’ collects all data
- Data is stored in a cloud
- Data is being marketed/invested
- Farmer gets a share of profit

31. Value Chain Integration: Monsanto’s
FieldScripts
PRESCRIPTIVE
FARMING
based on PRECISION
AGRICTULTURE

32. Farmers Business Network
• Owned by farmers
• Funded by Google
Ventures
2015:
7 million acres ground
Assessment of 500 seeds and
16 crops
Costs for farmer: $ 500 / year.

33. DATA-FAIR:
Open Software
Ecosystem
Stakeholders
Platforms
Apps + services
Knowledge models
Security, Privacy, Trust
Business models
Data sharing
Possible example of open collaboration
Farmer
Open Architecture & Infrastructure
Event-driven, Configurable, Customizable
Standards & Open Datasets
Real-time data sharing
IoT layer

34. Internet of Food and Farm 2020
2017-2020 - 70+ partners - 30M€ funding

35. IoF2020 – Large Scale Pilot

36. Wrap-up
 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
● Open infrastructure and software development and business
models are key issues
 Two extreme scenarios:
1. Strong integrated supply chain in which
farmer is franchiser/contractor with limited freedom
2. Open collaboration network in which a farmer is
empowered through easier switch between
suppliers and customers
Reality somewhere in between?
Farmer
Farmer

37. Thanks for your
attention!
Questions?
More information
sjaak.wolfert@wur.nl
nl.linkedin.com/in/sjaakwolfert/
Twitter: @sjaakwolfert
http://www.slideshare.net/SjaakWolfert