SlideShare a Scribd company logo
Sensors Going Smart
Smart Farming
1
Matthijs Vonder
• 1986 - 1992 UT-Electrotechnics
• 1992 - 1994 UT-Mechatronics (TWAIO)
• 1995 - 1998 Buhrs Zaandam
• 1998 - 2002 KPN Research
• 2003 - now TNO
Sr. Technical Scientist
Group: Monitoring & Control Services
Role: IT-Architect, Project leader
Large Scale (Infrastructure) Monitoring
• IJkdijk / Floodcontrol / Earthquakes
• SmartDairyFarming 1, 2, 3, (a.o. Young
Stock Rearing)
• Data Intensive Smart Agriculture Chains
(DISAC) – E-pieper
• IoT / Big Data (architectures/DDA)
matthijs.vonder@tno.nl
Contents
Introduction and positioning
Precision Dairy Farming
Data Sharing (via InfoBroker concept)
Some developments
Messages
Introduction and positioning
PF = Precision Farming (animals + agriculture )
PLF = Precision Lifestock Farming (animals, e.g pigs, poultry, cows)
PDF = Precision Dairy Farming (milk cows)
Project Smart Dairy Farming (SDF)
that is about PDF
Goal of project SDF1:
to support dairy farmers in the care of
individual animals.
with the specific goal of a longer
productive stay at the farm due to
improvement of individual health.
2
aka Smart
Farming
Starting point:
Cow centric thinking
Starting point:
Farmer in control
“De boer aan het roer”
Real time models / services
(at different providers)
Sensors from
different suppliers
Other data
sources
InfoBroker: Open platform
for (sensor) data producers
and consumers
Cow specific
workinstruction
(SOP)
Precision Dairy Farming
3
Some SDF1 – Facts & Figures
4
Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7
# cows/calves 459 186 315 239 706 202 351
Behaviour
Temperature
Activity
Milk production
Feed intake
Weight
Water intake
Milk intake
NB1: blue numbers are animals; not all animals are monitored for SDF (e.g. 3 and 4 only calves)
NB2: the left column gives a list of “sensor data categories” at a farm
NB3: numbers in black are the sensor fields within a category (e.g. 3 fields related to waterintake)
Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7
# cows/calves 459 186 315 239 706 202 351
Behaviour 5x 5x
Temperature 1x 1x
Activity 9x 9x 3x 6x 5x 13x 9x
Milk production 16x 20x 1x 2x 19x
Feed intake 24x 24x 10x 24x
Weight 10x 6x 6x 6x 7x 6x 10x
Water intake 3x 3x
Milk intake 7x 11x
See next
slide
Examples: young stock
5
Waterintake
life_number
date_time
Sensor
Tiks
daytikscounter
Weight
life_number
date_time
Sensor
Wcorr
Wstable
Wzero
Wavgmin
Wavgmax
Milkintake
life_number
date_time
Sensor
soll
soll_rm
abruf
SAUG
abbr_oz
besuch_ot
besuch_mt
Starting point:
Cow centric thinking
Starting point:
Farmer in control
“De boer aan het roer”
Real time models / services
(at different providers)
Sensors from
different suppliers
Other data
sources
InfoBroker: Open platform
for (sensor) data producers
and consumers
Cow specific
workinstruction
(SOP)
Precision Dairy Farming
Think big,
start small
6
Numbers for the Dutch situation:
• 15000+ farmers
• in total more then 1.5 million milk cows
• 20 to 200+ datafields per cow
• many different stakeholders in the chain
7
Data Sharing: InfoBroker concept
InfoBroker* functionalities:
Open interfaces for data exchange (API)
Authentication
who are you (are you allowed to login)
Permissions
which data may be used by whom
to be set by the farmers
Namingservice
location where the data can be found
Integration
combining info from different sources
Pay-per-use
fixed costs (connections)
variable costs (used data)
So:
no central datastore for (sensor)data!
but indeed a broker
and reduces/prevents duplication
cow specific work
instructions (SOPs)
InfoBroker
cow centric data
cow centric
data
Cow centric
Sensor data
Static data
(e.g. feed)
Cow centric
Sensor data
Static data
(e.g. date of birth)
Dashboard
Model
Model
Model
x 15.000+
* For InfoBroker see eg. EC-PLF paper (Vonder, van der Waaij, Harmsma, & Donker, 2015)
publications.tno.nl/publication/34623333/DBpkjn/vonder-2015-near.pdf
Some scenario’s for using the InfoBroker:
InfoBroker
Realtime-Model
(expert)
Farmer
What else?BenchmarkingCoöperation
Advisor
Static
data
Real time
cow-centric
data
8
GovernmentOther farmers
Some Smart Farming / Big (sensor) Data
developments (at TNO)
Big Data IT-architectures
For Multi Stakeholder Collaborations
from Research to Operations: “Think Big, Start small”
Semantic interoperability of (sensor) data
Linked Data, Ontology mapping, etc
Eg. Common Dairy Ontology (CDO)
Data Driven Analyses (DDA)
E.g. “roughage intake prediction”, “look-alike-cow”,
“pregnacy insemination failure prediction”
Interactive Visualisation of BD
Dashboards, pixel plots, etc
Virtual/Augmented reality (Hololens)9
Cooperatie Datahub
Operational implementation of InfoBroker,
permissions and more (live Q4 2017)
Messages
Use of sensor (and other) data (via an InfoBroker/Datahub) could be the
right direction for statistical products
Improve current statistics
Develop new statistics
Lower the response burden
A lot of data is already/soon available, for Dairy e.g.
Static and sensor data of dairy cows
Pasture behaviour-, financial-, environmental data
And more agrifood sub domains to come soon
Think about: What-is-in-it for the data-owner (e.g. farmer)?
Money (for the data)?
Lower response burden?
Benchmark-report (relative position compared to peers)?
O
One liner: Think Big, Start small !
Start experimenting now (and give feedback for additions/improvement)!
Work together with others (Multi Stakeholder Collaboration)
10
Thank you for your attention
info: matthijs.vonder@tno.nl
For more inspiration:
TIME.TNO.NL

More Related Content

Similar to Sensors Mathijs Vonder

Big Data and Data Science: The Technologies Shaping Our Lives
Big Data and Data Science: The Technologies Shaping Our LivesBig Data and Data Science: The Technologies Shaping Our Lives
Big Data and Data Science: The Technologies Shaping Our Lives
Rukshan Batuwita
 
Information management & ICT in Agri-Food
Information management & ICT in Agri-FoodInformation management & ICT in Agri-Food
Information management & ICT in Agri-Food
Sjaak Wolfert
 
IoT and the Future of work
IoT and the Future of work IoT and the Future of work
FIspace for Executive Board of Directors of Wageningen UR
FIspace for Executive Board of Directors of Wageningen URFIspace for Executive Board of Directors of Wageningen UR
FIspace for Executive Board of Directors of Wageningen UR
Sjaak Wolfert
 
FIspace at FInish matchmaking event
FIspace at FInish matchmaking eventFIspace at FInish matchmaking event
FIspace at FInish matchmaking event
Sjaak Wolfert
 
Data Governance in agriculture
Data Governance in agricultureData Governance in agriculture
Data Governance in agriculture
Krijn Poppe
 
IoT in agri-food
IoT in agri-foodIoT in agri-food
IoT in agri-food
Sjaak Wolfert
 
How IoT is changing the agribusiness landscape
How IoT is changing the agribusiness landscapeHow IoT is changing the agribusiness landscape
How IoT is changing the agribusiness landscape
Sjaak Wolfert
 
Fiware successes in Agriculture
Fiware successes in AgricultureFiware successes in Agriculture
Fiware successes in Agriculture
Walton Institute
 
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAIMAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI
Big Data Week
 
Big&open data challenges for smartcity-PIC2014 Shanghai
Big&open data challenges for smartcity-PIC2014 ShanghaiBig&open data challenges for smartcity-PIC2014 Shanghai
Big&open data challenges for smartcity-PIC2014 Shanghai
Victoria López
 
Mega Trends in the Food Sector
Mega Trends in the Food SectorMega Trends in the Food Sector
Mega Trends in the Food Sector
Kim Escherich
 
1_Introduction to IoT_Basic Terminologies.ppt
1_Introduction to IoT_Basic Terminologies.ppt1_Introduction to IoT_Basic Terminologies.ppt
1_Introduction to IoT_Basic Terminologies.ppt
Rizwan408930
 
The Internet of Food and Farm
The Internet of Food and FarmThe Internet of Food and Farm
The Internet of Food and Farm
Sjaak Wolfert
 
Future internet and agri at SRII Japan
Future internet and agri at SRII JapanFuture internet and agri at SRII Japan
Future internet and agri at SRII Japan
Krijn Poppe
 
IoT and BigData
IoT and BigDataIoT and BigData
IoT and BigData
Daan Gerits
 
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...
BigData_Europe
 
FDS Module I 20.1.2022.ppt
FDS Module I 20.1.2022.pptFDS Module I 20.1.2022.ppt
FDS Module I 20.1.2022.ppt
PerumalPitchandi
 
Creating self-aware and smart healthy cities
Creating self-aware and smart healthy citiesCreating self-aware and smart healthy cities
Creating self-aware and smart healthy cities
Maged N. Kamel Boulos
 
Towards data-driven agri-food business
Towards data-driven agri-food businessTowards data-driven agri-food business
Towards data-driven agri-food business
Sjaak Wolfert
 

Similar to Sensors Mathijs Vonder (20)

Big Data and Data Science: The Technologies Shaping Our Lives
Big Data and Data Science: The Technologies Shaping Our LivesBig Data and Data Science: The Technologies Shaping Our Lives
Big Data and Data Science: The Technologies Shaping Our Lives
 
Information management & ICT in Agri-Food
Information management & ICT in Agri-FoodInformation management & ICT in Agri-Food
Information management & ICT in Agri-Food
 
IoT and the Future of work
IoT and the Future of work IoT and the Future of work
IoT and the Future of work
 
FIspace for Executive Board of Directors of Wageningen UR
FIspace for Executive Board of Directors of Wageningen URFIspace for Executive Board of Directors of Wageningen UR
FIspace for Executive Board of Directors of Wageningen UR
 
FIspace at FInish matchmaking event
FIspace at FInish matchmaking eventFIspace at FInish matchmaking event
FIspace at FInish matchmaking event
 
Data Governance in agriculture
Data Governance in agricultureData Governance in agriculture
Data Governance in agriculture
 
IoT in agri-food
IoT in agri-foodIoT in agri-food
IoT in agri-food
 
How IoT is changing the agribusiness landscape
How IoT is changing the agribusiness landscapeHow IoT is changing the agribusiness landscape
How IoT is changing the agribusiness landscape
 
Fiware successes in Agriculture
Fiware successes in AgricultureFiware successes in Agriculture
Fiware successes in Agriculture
 
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAIMAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI
 
Big&open data challenges for smartcity-PIC2014 Shanghai
Big&open data challenges for smartcity-PIC2014 ShanghaiBig&open data challenges for smartcity-PIC2014 Shanghai
Big&open data challenges for smartcity-PIC2014 Shanghai
 
Mega Trends in the Food Sector
Mega Trends in the Food SectorMega Trends in the Food Sector
Mega Trends in the Food Sector
 
1_Introduction to IoT_Basic Terminologies.ppt
1_Introduction to IoT_Basic Terminologies.ppt1_Introduction to IoT_Basic Terminologies.ppt
1_Introduction to IoT_Basic Terminologies.ppt
 
The Internet of Food and Farm
The Internet of Food and FarmThe Internet of Food and Farm
The Internet of Food and Farm
 
Future internet and agri at SRII Japan
Future internet and agri at SRII JapanFuture internet and agri at SRII Japan
Future internet and agri at SRII Japan
 
IoT and BigData
IoT and BigDataIoT and BigData
IoT and BigData
 
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...
 
FDS Module I 20.1.2022.ppt
FDS Module I 20.1.2022.pptFDS Module I 20.1.2022.ppt
FDS Module I 20.1.2022.ppt
 
Creating self-aware and smart healthy cities
Creating self-aware and smart healthy citiesCreating self-aware and smart healthy cities
Creating self-aware and smart healthy cities
 
Towards data-driven agri-food business
Towards data-driven agri-food businessTowards data-driven agri-food business
Towards data-driven agri-food business
 

More from Centraal Bureau voor de Statistiek

Sensors by Maiki Ilves
Sensors by Maiki IlvesSensors by Maiki Ilves
Sensors by Maiki Ilves
Centraal Bureau voor de Statistiek
 
Happens online by Bastiaan Zijlema
Happens online by Bastiaan ZijlemaHappens online by Bastiaan Zijlema
Happens online by Bastiaan Zijlema
Centraal Bureau voor de Statistiek
 
Happens online Bastiaan Rooijakkers
Happens online Bastiaan RooijakkersHappens online Bastiaan Rooijakkers
Happens online Bastiaan Rooijakkers
Centraal Bureau voor de Statistiek
 
Useful by Piet Daas
Useful by Piet DaasUseful by Piet Daas
Happens online by Oscar Delnooz
Happens online by Oscar DelnoozHappens online by Oscar Delnooz
Happens online by Oscar Delnooz
Centraal Bureau voor de Statistiek
 
Mapping mobility Ioannis Tsalamanis
Mapping mobility Ioannis TsalamanisMapping mobility Ioannis Tsalamanis
Mapping mobility Ioannis Tsalamanis
Centraal Bureau voor de Statistiek
 
Mapping mobility Marco Puts
Mapping mobility Marco PutsMapping mobility Marco Puts
Mapping mobility Marco Puts
Centraal Bureau voor de Statistiek
 
Mapping mobility Piyushimita Thakuriah
Mapping mobility Piyushimita ThakuriahMapping mobility Piyushimita Thakuriah
Mapping mobility Piyushimita Thakuriah
Centraal Bureau voor de Statistiek
 
Sharing is caring
Sharing is caringSharing is caring
Sensors Ralph Meijers
Sensors Ralph MeijersSensors Ralph Meijers
Sensors Ralph Meijers
Centraal Bureau voor de Statistiek
 
Presentation Magchiel van Meeteren (ochtend)
Presentation Magchiel van Meeteren (ochtend)Presentation Magchiel van Meeteren (ochtend)
Presentation Magchiel van Meeteren (ochtend)
Centraal Bureau voor de Statistiek
 
Presentation Sofie De Broe (ochtend)
Presentation Sofie De Broe (ochtend)Presentation Sofie De Broe (ochtend)
Presentation Sofie De Broe (ochtend)
Centraal Bureau voor de Statistiek
 
stand van de woningmarkt
 stand van de woningmarkt  stand van de woningmarkt
stand van de woningmarkt
Centraal Bureau voor de Statistiek
 
6. parallelsessie 4 onderzoek doen met de bag
6. parallelsessie 4 onderzoek doen met de bag6. parallelsessie 4 onderzoek doen met de bag
6. parallelsessie 4 onderzoek doen met de bag
Centraal Bureau voor de Statistiek
 
6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries
6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries
6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries
Centraal Bureau voor de Statistiek
 
6. parallelsessie 2 groningen hanneke posthumus
6. parallelsessie 2 groningen hanneke posthumus6. parallelsessie 2 groningen hanneke posthumus
6. parallelsessie 2 groningen hanneke posthumus
Centraal Bureau voor de Statistiek
 
6. parallelsessie 1 duur scheefwonen kai gidding
6. parallelsessie 1 duur scheefwonen kai gidding6. parallelsessie 1 duur scheefwonen kai gidding
6. parallelsessie 1 duur scheefwonen kai gidding
Centraal Bureau voor de Statistiek
 
5. leegstand in nederland luc verschuren
5. leegstand in nederland luc verschuren5. leegstand in nederland luc verschuren
5. leegstand in nederland luc verschuren
Centraal Bureau voor de Statistiek
 
4. invloed van natuur michiel daams
4. invloed van natuur michiel daams4. invloed van natuur michiel daams
4. invloed van natuur michiel daams
Centraal Bureau voor de Statistiek
 
2. woningverkopen per regio farley ishaak
2. woningverkopen per regio farley ishaak2. woningverkopen per regio farley ishaak
2. woningverkopen per regio farley ishaak
Centraal Bureau voor de Statistiek
 

More from Centraal Bureau voor de Statistiek (20)

Sensors by Maiki Ilves
Sensors by Maiki IlvesSensors by Maiki Ilves
Sensors by Maiki Ilves
 
Happens online by Bastiaan Zijlema
Happens online by Bastiaan ZijlemaHappens online by Bastiaan Zijlema
Happens online by Bastiaan Zijlema
 
Happens online Bastiaan Rooijakkers
Happens online Bastiaan RooijakkersHappens online Bastiaan Rooijakkers
Happens online Bastiaan Rooijakkers
 
Useful by Piet Daas
Useful by Piet DaasUseful by Piet Daas
Useful by Piet Daas
 
Happens online by Oscar Delnooz
Happens online by Oscar DelnoozHappens online by Oscar Delnooz
Happens online by Oscar Delnooz
 
Mapping mobility Ioannis Tsalamanis
Mapping mobility Ioannis TsalamanisMapping mobility Ioannis Tsalamanis
Mapping mobility Ioannis Tsalamanis
 
Mapping mobility Marco Puts
Mapping mobility Marco PutsMapping mobility Marco Puts
Mapping mobility Marco Puts
 
Mapping mobility Piyushimita Thakuriah
Mapping mobility Piyushimita ThakuriahMapping mobility Piyushimita Thakuriah
Mapping mobility Piyushimita Thakuriah
 
Sharing is caring
Sharing is caringSharing is caring
Sharing is caring
 
Sensors Ralph Meijers
Sensors Ralph MeijersSensors Ralph Meijers
Sensors Ralph Meijers
 
Presentation Magchiel van Meeteren (ochtend)
Presentation Magchiel van Meeteren (ochtend)Presentation Magchiel van Meeteren (ochtend)
Presentation Magchiel van Meeteren (ochtend)
 
Presentation Sofie De Broe (ochtend)
Presentation Sofie De Broe (ochtend)Presentation Sofie De Broe (ochtend)
Presentation Sofie De Broe (ochtend)
 
stand van de woningmarkt
 stand van de woningmarkt  stand van de woningmarkt
stand van de woningmarkt
 
6. parallelsessie 4 onderzoek doen met de bag
6. parallelsessie 4 onderzoek doen met de bag6. parallelsessie 4 onderzoek doen met de bag
6. parallelsessie 4 onderzoek doen met de bag
 
6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries
6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries
6. parallelsessie 3 doorstromers op de woningmarkt paul_de_vries
 
6. parallelsessie 2 groningen hanneke posthumus
6. parallelsessie 2 groningen hanneke posthumus6. parallelsessie 2 groningen hanneke posthumus
6. parallelsessie 2 groningen hanneke posthumus
 
6. parallelsessie 1 duur scheefwonen kai gidding
6. parallelsessie 1 duur scheefwonen kai gidding6. parallelsessie 1 duur scheefwonen kai gidding
6. parallelsessie 1 duur scheefwonen kai gidding
 
5. leegstand in nederland luc verschuren
5. leegstand in nederland luc verschuren5. leegstand in nederland luc verschuren
5. leegstand in nederland luc verschuren
 
4. invloed van natuur michiel daams
4. invloed van natuur michiel daams4. invloed van natuur michiel daams
4. invloed van natuur michiel daams
 
2. woningverkopen per regio farley ishaak
2. woningverkopen per regio farley ishaak2. woningverkopen per regio farley ishaak
2. woningverkopen per regio farley ishaak
 

Recently uploaded

Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTESAdjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
Subhajit Sahu
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
mbawufebxi
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
ewymefz
 
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
yhkoc
 
一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单
enxupq
 
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
jerlynmaetalle
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
nscud
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
benishzehra469
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
AbhimanyuSinha9
 
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
vcaxypu
 
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
ewymefz
 
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
oz8q3jxlp
 
standardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghhstandardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghh
ArpitMalhotra16
 
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
slg6lamcq
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
pchutichetpong
 
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
slg6lamcq
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Subhajit Sahu
 
社内勉強会資料_LLM Agents                              .
社内勉強会資料_LLM Agents                              .社内勉強会資料_LLM Agents                              .
社内勉強会資料_LLM Agents                              .
NABLAS株式会社
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
balafet
 
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdfSample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Linda486226
 

Recently uploaded (20)

Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTESAdjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
 
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
 
一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单
 
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
 
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
 
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
 
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
 
standardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghhstandardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghh
 
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
 
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
 
社内勉強会資料_LLM Agents                              .
社内勉強会資料_LLM Agents                              .社内勉強会資料_LLM Agents                              .
社内勉強会資料_LLM Agents                              .
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
 
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdfSample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
 

Sensors Mathijs Vonder

  • 1. Sensors Going Smart Smart Farming 1 Matthijs Vonder • 1986 - 1992 UT-Electrotechnics • 1992 - 1994 UT-Mechatronics (TWAIO) • 1995 - 1998 Buhrs Zaandam • 1998 - 2002 KPN Research • 2003 - now TNO Sr. Technical Scientist Group: Monitoring & Control Services Role: IT-Architect, Project leader Large Scale (Infrastructure) Monitoring • IJkdijk / Floodcontrol / Earthquakes • SmartDairyFarming 1, 2, 3, (a.o. Young Stock Rearing) • Data Intensive Smart Agriculture Chains (DISAC) – E-pieper • IoT / Big Data (architectures/DDA) matthijs.vonder@tno.nl Contents Introduction and positioning Precision Dairy Farming Data Sharing (via InfoBroker concept) Some developments Messages
  • 2. Introduction and positioning PF = Precision Farming (animals + agriculture ) PLF = Precision Lifestock Farming (animals, e.g pigs, poultry, cows) PDF = Precision Dairy Farming (milk cows) Project Smart Dairy Farming (SDF) that is about PDF Goal of project SDF1: to support dairy farmers in the care of individual animals. with the specific goal of a longer productive stay at the farm due to improvement of individual health. 2 aka Smart Farming
  • 3. Starting point: Cow centric thinking Starting point: Farmer in control “De boer aan het roer” Real time models / services (at different providers) Sensors from different suppliers Other data sources InfoBroker: Open platform for (sensor) data producers and consumers Cow specific workinstruction (SOP) Precision Dairy Farming 3
  • 4. Some SDF1 – Facts & Figures 4 Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7 # cows/calves 459 186 315 239 706 202 351 Behaviour Temperature Activity Milk production Feed intake Weight Water intake Milk intake NB1: blue numbers are animals; not all animals are monitored for SDF (e.g. 3 and 4 only calves) NB2: the left column gives a list of “sensor data categories” at a farm NB3: numbers in black are the sensor fields within a category (e.g. 3 fields related to waterintake) Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7 # cows/calves 459 186 315 239 706 202 351 Behaviour 5x 5x Temperature 1x 1x Activity 9x 9x 3x 6x 5x 13x 9x Milk production 16x 20x 1x 2x 19x Feed intake 24x 24x 10x 24x Weight 10x 6x 6x 6x 7x 6x 10x Water intake 3x 3x Milk intake 7x 11x See next slide
  • 6. Starting point: Cow centric thinking Starting point: Farmer in control “De boer aan het roer” Real time models / services (at different providers) Sensors from different suppliers Other data sources InfoBroker: Open platform for (sensor) data producers and consumers Cow specific workinstruction (SOP) Precision Dairy Farming Think big, start small 6 Numbers for the Dutch situation: • 15000+ farmers • in total more then 1.5 million milk cows • 20 to 200+ datafields per cow • many different stakeholders in the chain
  • 7. 7 Data Sharing: InfoBroker concept InfoBroker* functionalities: Open interfaces for data exchange (API) Authentication who are you (are you allowed to login) Permissions which data may be used by whom to be set by the farmers Namingservice location where the data can be found Integration combining info from different sources Pay-per-use fixed costs (connections) variable costs (used data) So: no central datastore for (sensor)data! but indeed a broker and reduces/prevents duplication cow specific work instructions (SOPs) InfoBroker cow centric data cow centric data Cow centric Sensor data Static data (e.g. feed) Cow centric Sensor data Static data (e.g. date of birth) Dashboard Model Model Model x 15.000+ * For InfoBroker see eg. EC-PLF paper (Vonder, van der Waaij, Harmsma, & Donker, 2015) publications.tno.nl/publication/34623333/DBpkjn/vonder-2015-near.pdf
  • 8. Some scenario’s for using the InfoBroker: InfoBroker Realtime-Model (expert) Farmer What else?BenchmarkingCoöperation Advisor Static data Real time cow-centric data 8 GovernmentOther farmers
  • 9. Some Smart Farming / Big (sensor) Data developments (at TNO) Big Data IT-architectures For Multi Stakeholder Collaborations from Research to Operations: “Think Big, Start small” Semantic interoperability of (sensor) data Linked Data, Ontology mapping, etc Eg. Common Dairy Ontology (CDO) Data Driven Analyses (DDA) E.g. “roughage intake prediction”, “look-alike-cow”, “pregnacy insemination failure prediction” Interactive Visualisation of BD Dashboards, pixel plots, etc Virtual/Augmented reality (Hololens)9 Cooperatie Datahub Operational implementation of InfoBroker, permissions and more (live Q4 2017)
  • 10. Messages Use of sensor (and other) data (via an InfoBroker/Datahub) could be the right direction for statistical products Improve current statistics Develop new statistics Lower the response burden A lot of data is already/soon available, for Dairy e.g. Static and sensor data of dairy cows Pasture behaviour-, financial-, environmental data And more agrifood sub domains to come soon Think about: What-is-in-it for the data-owner (e.g. farmer)? Money (for the data)? Lower response burden? Benchmark-report (relative position compared to peers)? O One liner: Think Big, Start small ! Start experimenting now (and give feedback for additions/improvement)! Work together with others (Multi Stakeholder Collaboration) 10
  • 11. Thank you for your attention info: matthijs.vonder@tno.nl For more inspiration: TIME.TNO.NL