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Transcript of IoT and other disruptive technologies
The Future of Farming and Food: Internet of Things, Block chain and other
disrupting technologies
Krijn Poppe Wageningen Economic Research
Based on work with WUR team (Sjaak Wolfert, Cor Verdouw, Lan Ge, Marc
Jeroen Bogaardt, Jan Willem Kruize and others)
October 2017 Cornell University, NY, USA
Wageningen University & Research
Academic research & education, and applied research
5,800 employees (5,100 fte)
>10,000 students (>125 countries)
Several locations
Turnover about € 650 million
Number 1 Agricultural University for the 4th year in a row
(National Taiwan Ranking)
To explore the potential of nature to improve the quality of life
Krijn J. Poppe
Economist
Research Manager at Wageningen Economic Research
Member of the Council for the Environment and Infrastructure
(foto: Fred Ernst)
Member Advisory Committee Province of South-Holland on the
quality of the Living Environment
Board member of SKAL – Dutch organic certification body
Fellow EAAE. Former Secretary General of the EAAE, now involved
in managing its publications (ERAE, EuroChoices)
Former Chief Science Officer Ministry of Agriculture
Content of the presentation
What is happening: disruptive ict trends leading to data capturing
Why does that happen now: long wave theory
New players challenge food chains
Platforms
Blockchain
How we support innovation in the EU
Our approach to support innovation
Disruptive ICT Trends:
Mobile/Cloud Computing – smart phones, wearables, incl. sensors
Internet of Things – everything gets connected in the internet (virtualisation, M2M, autonomous devices)
Location-based monitoring - satellite and remote sensing technology, geo information, drones, etc.
Social media - Facebook, Twitter, Wiki, etc.
Block Chain – Tracing & Tracking, Contracts.
Big Data - Web of Data, Linked Open Data, Big data algorithms
High Potential for unprecedented innovations!
everywhere
anything
anywhere
everybody
• Products change: the tractor withICT – from product to service
• New products: smart phones, apps, drones: should markets becreated or regulated ?
New entrants:• Designers on Etsy• Landlords on AirBnb• Drivers on Uber
New entrants:• Direct international
sales by website• Long tail: buyers for
rare products
• Due to ICT new options to fine tune regulation / monitor behaviour
• Regulation can be out of date
• New types of pricing and contracts: on-lineauctions, dynamic pricing, risk profiling etc.
• Shorter supply chains (intermediaries as travel agencies and book shops disappear)
• Strong network effects in on-line platforms (rents and monopolies)
Prescriptive AgriculturePredictive Maintenance
10
IoT in Smart Farming
cloud-based event and data
management
smart sensing& monitoring
smart analysis & planning
smart control
Virtual Box
Location A Location B
Location
& State
update
Location &
State
update Location
& State
update
IoT in Agri-Food Supply Chains
11Drones, Big Data and Agriculture
IoT and the consumer: food and health
Smart Farming
Smart Logistics
tracking & tracing
Domotics Health
Fitness/Well-being
Towards smart autonomous objects
Source: Deloitte (2014), IT Trends en Innovatie Survey
Tracking & Tracing
Monitoring
I am thirsty: water me within 1 hour!
I am product X at locatie L of Z
My vaselife is optimal at a
temperature of 4,3 °C.
I am too warm: lower the
temperature by3 °C
Event Management
I am too warm: I lowerthe cooling of my truck
X by 2 °C.
I don’t want tostand besidesthat banana!
I am thirsty!
I am warm!
Optimalisation
Autonomy
Tuinbouw Digitaal © 2013
BIG
DATA
OPEN
DATA
TRANSFORM
MAP
ANONY MISE
AGGRE GATE
STRUCTURE
COMBINE
INTER PRETERE
GATHER
Social media - Unstructured - Event-driven Informatiesystems–Structured - Transaction-driven
Big data analysis: Machine learning
17
5 important techniques in artificial intelligence:
1. Symbolic reasoning2. Connections modeled on the basis of the brain'sneurons3. Evoloutionary algorithms that test variation4. Bayesian inference5. Sytems that learn by analogy
tijd
Mate van verspreiding
van technologische revolutie
Installatie periode
Volgende
golf
Uitrol periode
Draai-
punt
INDRINGER
EXTASE
SYNERGIE
RIJPHEID
Door-
braak
WerkeloosheidStilstand oude bedrijfstakken
Kapitaal zoekt nieuwe techniek
Financiele bubbleOnevenwichtighedenPolarisatie arm en rijk
Gouden eeuwCoherente groei
Toenemende externalities
Techniek bereikt grenzenMarktverzadiging
Teleurstelling en gemakzucht
Institutionele
innovatie
Naar Perez, 2002
Crash
2008
1929
1893
1847
1797
time
Degree of diffusion of the
technological revoluton
Installation period
Next
wave
Deployment
period
Turning
point
IRRUPTION
FRENZY
SYNERGY
MATURITY
Big Bang
Unemployment
Decline of old industries
Capital searches new techniques
Financial bubbleDecoupling in the system
Polarisation poor and rich
Golden age
Coherent growth
Increasing externalities
Last products & industries
Market saturation
Disappointment vscomplacency
Crash
2008
1929
1893
1847
1797
Institutional
innovation
Based on Perez, 2002
The opportunity for green growth
1971 chip ICT1908 car, oil, mass production1875 steel1829 steam, railways1771 water, textiles
4 grand challenges: tomorrow’s business
Transport
Input industriesFarmer
Food processor Retail / consumerSoftwareprovider
Logistic solu-tion providers
Collaboration and Data Exchange is needed!
Food & nutrition
securityClimate
change
Healthy diet
for a healthy
life
Environmental
issues
Food chain: 2 weak spots – opportunity?
Input industriesFarmerFood processorConsumer Retail
• Public health issues –obesity, Diabetes-2 etc.
• Climate change asks for changes in diet
• Strong structural change
• Environmental costs need to be internalised
• Climate change (GHG) strengthens this
Is it coincidence that these 2 are the weakest groups?Are these issues business opportunities and does ICT help?
Content of the presentation
What is happening: disruptive ict trends leading to data capturing
Why does that happen now: long wave theory
New players challenge food chains
Platforms
Blockchain
How we support innovation in the EU
Our approach to support innovation
Dynamic landscape of Big Data & Farming
22
Farm
Farm
Farm
Farm
Data
Start-ups
Farming
AgBusinessMonsanto
Cargill
Dupont...
ICT Companies
GoogleIBM
Oracle
...
Ag TechJohn Deere
Trimble
Precision planting...
ICT
Start-upsFarm
Ag software
Companies
AgTech
Start-upsVenture Capital
Founders Fund
Kleiner PerkinsAnterra
...
Farm
Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
25
3. Smart, connected product
+
+
+
2. Smart Product
1. Product
Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
26
5. System of systems
farmmanagement
system
farm equipment
system
weather data
system
irrigation system
seed optimizing
system
fieldsensors
irrigation nodes
irrigation application
seedoptimizationapplication
farmperformance
database
seeddatabase
weather dataapplication
weatherforecasts
weathermaps
rain, humidity,temperature sensors
farm equipment
system
planters
tillers
combine
harvesters
4. Product system
Is this
‘mono-equipment
system’ reality?
How to cope with
changes in industry
boundries?
How many
platforms should
users and
developers enter?
Effects on Chain organisation
27
ICT lowers transaction costs
• In social media (Facebook etc.): the world is flat
with spiky metropolises
• In ‘sharing’ platforms (peer-to-peer like AirBnb,
Uber, crowd funding): creates new suppliers
(reduce overcapacity) and users. Long tail effects.
• In chain organisation: centralisation to grab
advantages of data aggregation or more markets?
• Platforms: centralisation via data management
Programmability: Low High
Asset specifity: Low High Low High
Contribution
partners
separable
High spot long-t. spot joint
market contract mrkt venture
Low coope- coop./ inside vertical
ration vertical contract owner-
© Boehlje ownership ship
Organisational arrangements in the food
chain are changing
Chain organisation changes (©Gereffi et al., 2005)
inputs
End p
roduct
PRICE
Shops
Complete Integration
Lead company
Leadcompany
Turnkeysupplier
Relationalsupplier
Market Modular Relational Captive Hierarchy
Low Degree of explicit coordination and power asymmetry High
Leadcompany
Farmers
2 Scenarios, with significant impacts ?
1. Scenario CAPTIVE PRODUCT CHAINS:
● Farmer becomes part of one integrated supply chain as a
franchiser/contractor with limited freedom
● one platform for potato breeder, machinery company, chemical
company, farmers and french fries processor.
● Weak integration with service providers, government ?
2. Scenario OPEN NETWORK COLLABORATION:
• Market for services, apps and data
• Common, open platform(s) are needed
• Higher upfront, common investment ??
• Business model of such a platform more difficult?
• More empowerment of farmers and cooperatives?
F
F
Content of the presentation
What is happening: disruptive ict trends leading to data capturing
Why does that happen now: long wave theory
New players challenge food chains
Platforms
Blockchain
How we support innovation in the EU
Our approach to support innovation
There is a need for
software ecosystems
for ABCDEFs:
Agri-Business
Collaboration & Data
Exchange Facilities
• Large organisations have gone digital, with ERP systems
• But between organisations (especially with SMEs) data exchange and interoperability is still poor
• ABCDEF platforms help
law & regulation
innovation
geographic
cluster
horizontal
fulfillment
Vertical
Platforms as central nodes in network
economy: some agricultural examples
• Fieldscripts (Monsanto)
• Farm Business Network (start-up with Google Ventures)
• Farm Mobile (start-up with venture capitalist): strong emphasis on data ownership
• Agriplace (start up by a Dutch NGO with a sustainability compliance objective)
• DISH RI – Richfields (consumer data on food, lifestyle and health)
• FIspace (recently completed EU project ready for commercialisation via a Linux-like Open Source model)
Note the different business models / governance structures!
Agriplace –compliance in food safety etc. made easy
Two platform examples from our work
Donate to (citizen) research
RICHFIELDS: manage yourfood, lifestyle, health data and donate data toresearch infrastructure
audit
FMIS
FIspace: an eco-system of apps to push
data
FARMER SCANS PESTICIDES PACKAGE IN THE FIELD
APP CONNECTS BASF FOR E-INSTRUCTION, CROP AND SOIL SPECIFIC
APP ASK METEO FOR 24 hour WEATHER FORECAST
BASF SENDS INSTRUCTION TO SPRAYING MACHINE ON WATER / PESTICIDE RATIO >> Machine adjusts
APP CHECKS ADVISE WITH GOV.AGENCY
FARMER CAN SHARE DATA WITH GOVERNMENT, SGS-AUDITOR GLOBAL GAP AND PUBLIC
CAN I USE MY CURRENT
SERVICE ?
CAN I USE MY FMS ?
DOES IT WORK WITH
BAYER / DEERE
DOES IT WORK WITH
BRC / ISAcert
Can we link apps / services in a clever way ?
Leading to a market for services (apps and
data)?
Can this market be European (not MS), so
that development costs of services (apps and
data) are shared ?
Collaborative infrastructure
Scenario: get expert advice for spraying to handle disease on tomatoes
State AuthorityFranz Farmer Ed Expert
Spraying (follow advice)
Create Advice
Approval
Request Advice
Co
llab
ora
tive
Bu
sin
ess
Pro
cess
1
2
3
FIspace App
‘Weather Information’
FIspace App
‘Spraying Expert Advice’
FIspace App
‘Spraying Certification’
Bac
k-En
d S
yste
ms
Farm Management
Systems
Sensor Network in the Greenhouse
Agronomist Expert System
Regulations & Approval System
product type, etc.
sensor data (access details)
suggested chemical
advice details
certificationdetails
36
Towards highly integrated solutions
Platforms in the cloud of input suppliers and food processors:• What is the scope (connect only machinery or also with chemical
companies and accountants ?)• Reduce costs of linking individually with many other platforms and
software packages (especially in chains that are not integrated)• Is it possible to use apps with their own business model, so that the
platform does not have to pay all their costs? >> can (non-strategic) apps be available on several platforms?
• How to prevent that farmers complain to have to pay for basis apps (e.g. weather service) more than once?
MyJohnDeere.com Farmers
Biz architectbundles apps in a platform
...
80 Accelerator companies
Apps
Towards highly integrated solutions
Highly Integrated Service Solutions• Event-driven• Configurable• Customizable• Service model
Data (Standardisation) Services
AdaptEPCIS
MyJohnDeere.com
Data Standardsto connect
BusinessCollaborationServices -Based on OpenSource Software
Farmers
Biz architectbundles apps in a platform
...
80 Accelerator companies
Apps
Modules:Single SignOnBiz Collab.Event Proces.System-Data integrationApp repository
FIspaceApp Store
80 Accelerator companies
Configure &Use Systems
First Commercial MVP by ... ?
App developer Business Configurator End User
Advertiser
Access fee
Use Fee Use Fee
Access fee (e.g. CargoSwApp)
Pay for app use (e.g. Spraying Advice)
Sponsored app
FIspace FoundationMVP – open source
My JohnDeere365 Farmnet
AkkerwebDacom/CROP-R
Datalab Pantheon
ICT company Service model ?
Value propositionPlatforms solve the issue of connecting individually with a lot of business
partners to exchange data : connect easily to apps (and data services in apps) based on EDI-standards or let farmers / end-users make the connection
App-developers Develop one app for different platforms Reach a European / Global market
Governments (and industry organisations)
See above for your government platform (paying agency, public advisory service etc.)Promote innovation by a competitive market for apps with new servicesPrevent lock-inn situations for farmers and unbalanced power relations in the information exchange in food chains
Farmers Not a direct FIspace client. Platforms using FIspace inside provide you more choice
Software writers in platforms and app-companies
Helps you to be part of an open source community that cares for sustainable food production with up to date ICT – be recognized by your peers
Towards highly integrated solutions
Highly Integrated Service Solutions• Event-driven• Configurable• Customizable• Service model
Data (Standardisation) Services
AdaptEPCIS
MyJohnDeere.com
Data Standardsto connect
BusinessCollaborationServices -Based on OpenSource Software
Farmers
Biz architectbundles apps in a platform
...
80 Accelerator companies
Apps
Modules:Single SignOnBiz Collab.Event Proces.System-Data integrationApp repository
Is this commercially feasible?
Or is it too much a common pool
investment in a market where
everybody wants to grab a stake, over-
estimates the value of its own data and
finds it easier to builds its own website
?
Content of the presentation
What is happening: disruptive ict trends leading to data capturing
Why does that happen now: long wave theory
New players challenge food chains
Platforms
Blockchain
How we support innovation in the EU
Our approach to support innovation
Data in Blockchains
No 3rd party needed for Network Administrative Organization Distributed Automated Organization
● Higher transparency and credibility
● No current agri-food/ICT player is dominating
● Attractive/easy for small players to step in (inclusiveness)
● Less personal
Smart contracts: data is automatically exchanged according to pre-set agreements and rules
General: privacy and security can be better guaranteed
....
44
Towards Blockchain for a tangled web?
Case: Table Grapes produced in South Africa for the Dutch market
Proof of Concept on case Table Grapes
• Feasibility of tracking certificates and ownership in
blockchain through smart contracts
• Demonstrating distributed database & immutability
• Demonstrating transparency on business rules and the
validity of certificates
• Added value
• Fraud detection and prevention
• Increased value of certificates
• Inclusive development
• But it will not solve all fraud in the food chain
Content of the presentation
What is happening: disruptive ict trends leading to data capturing
Why does that happen now: long wave theory
New players challenge food chains
Platforms
Blockchain
How we support innovation in the EU
Our approach to support innovation
What is going on in the European Union cs.
• EU SCAR AKIS Towards the future – a foresight paper, 2015• ERA-NET ICT AGRI: strategic research agenda• Future Internet PPP
• Smart AgriFood, Fispace• Accelerator projects: Finish, SmartAgrifood2, Fractals
• H2020: Internet-of-Farm &Food-2020: Internet of
Things (30 mln.)
• European Innovation Partnership: seminar data driven
data models (Sofia) + benchmarking
• FNH-RI en RICHFIELDS: consumer data on food,
lifestyle and health
• Policy advice (OECD, EU Parliament, Dutch gov.)• Plus several other projects in H2020 where ict is an
important work package (e.g. Valerie)
OBJECTIVE
IoF2020 fosters a large-scale uptake
of IoT in the European farming and
food sector
• Demonstrate the business case of
IoT for a large number of application
areas in farming and food sector;
• Integrate and reuse available IoT
technologies by exploiting open
infrastructures and standards;
• Ensure user acceptability of IoT
solutions in farming and food sector
by addressing user needs, including
security, privacy and trust issues;
• Ensure the sustainability of IoT
solutions beyond the project by
validating the related business
models and setting up an IoT
ecosystem for large scale uptake.
55
IOF2020 IN BRIEF
56
16
COUNTRIES
4 YEARSStart = January
2017
€35 MILLION
BUDGET(€30 million co-funded
under EU H2020
programme)
71 PARTNERS
ORGANISATIONS
Optimizing cultivation and processing of wine by sensor-actuator networks and
big data
analysis within a cloud framework
BIG WINE OPTIMIZATION
OBJECTIVE
Deploy an IoT system
• based on 150
actuator/sensor nodes
• to monitor and gather
the data coming from 5
vineyards and cellars
• to perform data analysis
and decision making
• to improve the vine yield
and wine production
IOT SYSTEM
ARCHITECTURE
• Fixed and mobile sensors to monitor weather, vineyard and wine conditions
• Middleware to collect and analyse sensor data and actuate
• Applications to facilitate the decision making to monitor and control the vineyards and wine anytime and anywhere.
Real-time monitoring and control of water supply and crop protection of table
grapes and predicting shelf life
FRESH TABLE GRAPES CHAIN
Yield +15% | Crop value +10% | Water usage -20% | Shelf life +20% Harvest rejection -20% | Post-harvest rejection -10%
Management Information Layer
Operations Execution Layer
Production Control Layer
Physical Object Layer
Actuate Sense
Analyse Fertiliser Pesticide Need
Plan Crop Protection
Farm ControlMonitor Crop Growth and Postharvest
Predict Yield (only
for crop growth)
Plan Harvesting
Definition Management
Detailed Scheduling
Execution Management
Data Collection
Data Analysis
Control Spraying Machine
Sense Crop Growth
Control irrigation system
Fertilisers and Pesticides Growing Crops
Irrigation System
FieldSpraying machine
Farmer Agronomic Engineer
machine settings
task instructions field sensor data Irrigation system data
machine requirementstask definition
aggregated data
Post Harvest System
Weather station
Sense Weather
Control Post Harvest
task instructionsSense Chemical and Physical Parameters
postharvest system data
Objective
Provide the pig farmers with crucial information to effectively steer their
management to reduce boar taint, health problems, increase productivity
IOF2020 ECOSYSTEM & COLLABORATION SPACE
WP
1 P
roje
ct
Coord
ina
tion
&
Ma
na
ge
me
nt
GENERIC APPROACH & STRUCTURE
WP2 Trials/Use cases: Knowledge & App developmentLean multi-actor approach
3. EVALUATION
1. CO-DESIGN2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
WP3 IoT Integration WP4 Business Support
WP5 Ecosystem Development
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
Pro
ject
level
Use c
as
e l
evel
sustain
reuse
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
lorem 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
OUTSIDE PROJECT
OPEN CALL
TOWARDS TO THE IOF2020 ECOSYSTEM
GENERAL PUBLIC
AND MEDIA
POLICY-MAKERS
AND REGULATORS
SCIENTIFIC
COMMUNITY
AGRICULTURAL (INDEPENDENT)
ADVISORY SERVICES
NGOS & INTEREST
ORGANISATIONS
IOT TECHNOLOGY
PROVIDERS
BUSINESS SUPPORT
ORGANISATIONS• Accelerators
• Incubators
• Chambers of commerce
• Enterprises networks
END-USERS• Farm equipment suppliers
• Food processing companies
• Retailers
• Transporters
• Consumers’ associations
INVESTORSFARMERS
COOPERATIVES CONSORTIUM PARTNERS
ICT research group: 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
Business models, data ethics and governance as special focus
ECOSYSTEM & COLLABORATION SPACE
Pro
ject
Coord
ination
& M
anagem
ent
Our Approach...
Trials/Use Cases: Knowledge & App developmentLean multi-actor approach
3. EVALUATION
1. CO-DESIGN2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
Technical IntegrationBusiness Modelling &
Governance
Ecosystem Development
Elements for an Agri-ICT research strategy
• Promote data-exchange (reduce administrativeburdens, create value via combination, aggregation)• Standardisation for interoperability; AgGateway, UN/CEFACT• Platform(s) for data exchange• Open data by government
• Promote innovation with new services• Especially ict-start ups, connect them with farmers and
companies (e.g. FIware 3 stage approach)• Internet of Things• Big Data (use of social media data, machine learning etc.) ?
• Advisory service: “just” another player in data exchange + update own software: go real time
• Research: support all this + real time agronomicmodels.
Don’t forget:• The interstates made the cars flowing, changed our way of
living more (specialisation, suburbs etc.) than the car itself.• We need utilities in rural areas: 3G/4G/5G, but also ABCDEF
platforms to combine and aggregate data for value creationand to create markets for apps
• It raises issues of data governance (business model, data ownership, organisation model) as (vendor)platforms are only linked to one part of the farm and can be natural monopolies with lock-in effects
• Solutions (market-based or otherwise) are contingent on situation and institutional environment
Thanks for your
attention
and we welcome
collaboration in
your projects !!
www.wur.nl