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Agri-IoT: A semantic framework for IoT-
enabled Smart Farming Applications
^ Unit for Reasoning, Querying and
Stream Processing
Insight Centre for Data Analytics,
National University of Ireland,
Galway
Andreas Kamilaris*, Feng Gao^, Francesc X. Prenafeta-Boldu* ́, Ali Intizar^
World Forum IoT, Reston, VA, USA- December 12-14, 2016
* GIRO Joint Research Unit
IRTA-UPC
Barcelona, Spain
Nowadays Sensors are almost Everywhere
14/12/2016
• Cheap and Light-weight
Sensors
• Easy Installation
• Sensors embedded into
different devices
• Varying from a smart phone
to heavy machinery.
2
Images Source: google.com
IoT-Forum 2016, Reston, VA
Sensors for Smart Farming
14/12/2016
• In smart farming, mostly
environmental sensors are
deployed
• Weather, Wind Speed, Soil
Moisture Level, Temperature
etc.
• Animal Health Monitoring
• Biodegradeable Sensors
3IoT-Forum 2016, Reston, VA
Data Streams are Everywhere
14/12/2016
• Sensors produce
enormous amount of data
• Mostly in a streaming
fashion
• High velocity
• Data needs to be
harnessed and analysed
effectively
4IoT-Forum 2016, Reston, VA
Smart Farming Applications
14/12/2016 5IoT-Forum 2016, Reston, VA
Smart Farming: An Overview
14/12/2016 6IoT-Forum 2016, Reston, VA
Smart Farming IoT Data Analytic Platforms
14/12/2016 7
• Different IoT Platforms
for Smart Farming have
been proposed
• Mostly commercial
products
• Support to collect and
store data in the cloud
• Visualization and
Dashboards for analytics
IoT-Forum 2016, Reston, VA
IoT Platforms for Smart Farming: An Eco-System
14/12/2016 8IoT-Forum 2016, Reston, VA
Key Data Challenges
• High variability of data in the agri-food domain
• Data combination and information fusion is a taunting task
• Variable Observation Rates
• Un-Predictability
• Platforms Heterogeneity
• Silos Architectures
• Description of data is very poor – lack of semantics.
• High volumes of data – low availability of big data real-time
analytics tools.
14/12/2016 9IoT-Forum 2016, Reston, VA
Potential Achievements using Agri-IoT Platforms
• Precision agriculture:
• Improves productivity
• Reduce resource consumption
• Environmental impact
• Real-time Data Analytics
• On-demand data integration
• Real-time event detection systems
• Alert and notification generation
• Predictive Analytics
• Data analytics techniques to predict events
• Provide support for pattern analysis
14/12/2016 10IoT-Forum 2016, Reston, VA
Agri-IoT: Real-time Data Analytics Platform
14/12/2016 11
• Integration of heterogeneous IoT data streams
• Provision of data processing pipeline for real-time data
analytics
• Support for innovative smart farming applications
IoT-Forum 2016, Reston, VA
Agri-IoT: Real-time Data Analytics Platform
14/12/2016 12IoT-Forum 2016, Reston, VA
Agri-IoT: Software Components
14/12/2016 13
• Flexible Modular
Components
• API based Access
• Plug and play support for
different components
• Semantic based
reasoning and event
detection
• Scalability in a distributed
infrastructure
IoT-Forum 2016, Reston, VA
Agri-IoT: Software Components
14/12/2016 14
• Reusability of Components
– Data wrapper: (source: CityPulse)
– Device manager: (source: FIWARE IoT Back-end)
– Discovery module: (source: FIWARE IoT Edge)
– Data aggregation: (source: IoT-A)
– Data federation: (source: CityPulse)
– Event detection: (source: CityPulse)
– Real-time adaptive reasoning: (source: CityPulse)
– External agent: (source: Agri-IoT)
– Dashboard: (source: ThingSpeak, freeboard)
– Mobile Apps: (source: MapYourMeal, FoodLoop)
– Knowledge base: (source: OpenIoT)
IoT-Forum 2016, Reston, VA
Agri-IoT: Semantic Annotation
14/12/2016 15
• SSN Ontology for Sensor Data Representation
• OWL-S for Service Description
• AgroVoC, Agri Ontology Service and AgOnt
IoT-Forum 2016, Reston, VA
Scenario 1: Fertility Management
14/12/2016 16
• Monitoring Breeding
Cycle
• Pre-mating Heat
Detection
• Real-time Monitoring
major milestones
• Prediction for resource
requirements
• Future production
forecasting
IoT-Forum 2016, Reston, VA
Scenario 1: Fertility Management
14/12/2016 17
• A single farm of 1,000
cows.
• Periodic heat detection
using wearable sensors
• Automated selection of
cows in the age-range of
18 to 30 months.
• Real-time event detection
and notification systems
IoT-Forum 2016, Reston, VA
Scenario 2: Soil Fertility
14/12/2016 18
• Soil Composition
Monitoring
• Soil Index
• Salinity
• Moisture
• Real-time Observation
Monitoring
• Integration with External
Sources e.g. weather,
rainfall etc.
IoT-Forum 2016, Reston, VA
Scenario 2: Soil Fertility
14/12/2016 19
• Real-time Alert
Notification System
• Water Management
• Right Time for cultivation
• Fertilization
IoT-Forum 2016, Reston, VA
Conclusion
14/12/2016 20
• Agri-IoT offers real-time data analytics for smart farming
applications.
• Deployed a tested in medium-to-large farms (100-300
sensors deployed at the field).
• Tested for performance and scalability for real-time stream
processing and reasoning, based on IoT and semantic web
technologies
• Can help farmers in decision making and fast reactions to
events happening.
• A platform for innovation using Agri Data.
IoT-Forum 2016, Reston, VA

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Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications

  • 1. Agri-IoT: A semantic framework for IoT- enabled Smart Farming Applications ^ Unit for Reasoning, Querying and Stream Processing Insight Centre for Data Analytics, National University of Ireland, Galway Andreas Kamilaris*, Feng Gao^, Francesc X. Prenafeta-Boldu* ́, Ali Intizar^ World Forum IoT, Reston, VA, USA- December 12-14, 2016 * GIRO Joint Research Unit IRTA-UPC Barcelona, Spain
  • 2. Nowadays Sensors are almost Everywhere 14/12/2016 • Cheap and Light-weight Sensors • Easy Installation • Sensors embedded into different devices • Varying from a smart phone to heavy machinery. 2 Images Source: google.com IoT-Forum 2016, Reston, VA
  • 3. Sensors for Smart Farming 14/12/2016 • In smart farming, mostly environmental sensors are deployed • Weather, Wind Speed, Soil Moisture Level, Temperature etc. • Animal Health Monitoring • Biodegradeable Sensors 3IoT-Forum 2016, Reston, VA
  • 4. Data Streams are Everywhere 14/12/2016 • Sensors produce enormous amount of data • Mostly in a streaming fashion • High velocity • Data needs to be harnessed and analysed effectively 4IoT-Forum 2016, Reston, VA
  • 5. Smart Farming Applications 14/12/2016 5IoT-Forum 2016, Reston, VA
  • 6. Smart Farming: An Overview 14/12/2016 6IoT-Forum 2016, Reston, VA
  • 7. Smart Farming IoT Data Analytic Platforms 14/12/2016 7 • Different IoT Platforms for Smart Farming have been proposed • Mostly commercial products • Support to collect and store data in the cloud • Visualization and Dashboards for analytics IoT-Forum 2016, Reston, VA
  • 8. IoT Platforms for Smart Farming: An Eco-System 14/12/2016 8IoT-Forum 2016, Reston, VA
  • 9. Key Data Challenges • High variability of data in the agri-food domain • Data combination and information fusion is a taunting task • Variable Observation Rates • Un-Predictability • Platforms Heterogeneity • Silos Architectures • Description of data is very poor – lack of semantics. • High volumes of data – low availability of big data real-time analytics tools. 14/12/2016 9IoT-Forum 2016, Reston, VA
  • 10. Potential Achievements using Agri-IoT Platforms • Precision agriculture: • Improves productivity • Reduce resource consumption • Environmental impact • Real-time Data Analytics • On-demand data integration • Real-time event detection systems • Alert and notification generation • Predictive Analytics • Data analytics techniques to predict events • Provide support for pattern analysis 14/12/2016 10IoT-Forum 2016, Reston, VA
  • 11. Agri-IoT: Real-time Data Analytics Platform 14/12/2016 11 • Integration of heterogeneous IoT data streams • Provision of data processing pipeline for real-time data analytics • Support for innovative smart farming applications IoT-Forum 2016, Reston, VA
  • 12. Agri-IoT: Real-time Data Analytics Platform 14/12/2016 12IoT-Forum 2016, Reston, VA
  • 13. Agri-IoT: Software Components 14/12/2016 13 • Flexible Modular Components • API based Access • Plug and play support for different components • Semantic based reasoning and event detection • Scalability in a distributed infrastructure IoT-Forum 2016, Reston, VA
  • 14. Agri-IoT: Software Components 14/12/2016 14 • Reusability of Components – Data wrapper: (source: CityPulse) – Device manager: (source: FIWARE IoT Back-end) – Discovery module: (source: FIWARE IoT Edge) – Data aggregation: (source: IoT-A) – Data federation: (source: CityPulse) – Event detection: (source: CityPulse) – Real-time adaptive reasoning: (source: CityPulse) – External agent: (source: Agri-IoT) – Dashboard: (source: ThingSpeak, freeboard) – Mobile Apps: (source: MapYourMeal, FoodLoop) – Knowledge base: (source: OpenIoT) IoT-Forum 2016, Reston, VA
  • 15. Agri-IoT: Semantic Annotation 14/12/2016 15 • SSN Ontology for Sensor Data Representation • OWL-S for Service Description • AgroVoC, Agri Ontology Service and AgOnt IoT-Forum 2016, Reston, VA
  • 16. Scenario 1: Fertility Management 14/12/2016 16 • Monitoring Breeding Cycle • Pre-mating Heat Detection • Real-time Monitoring major milestones • Prediction for resource requirements • Future production forecasting IoT-Forum 2016, Reston, VA
  • 17. Scenario 1: Fertility Management 14/12/2016 17 • A single farm of 1,000 cows. • Periodic heat detection using wearable sensors • Automated selection of cows in the age-range of 18 to 30 months. • Real-time event detection and notification systems IoT-Forum 2016, Reston, VA
  • 18. Scenario 2: Soil Fertility 14/12/2016 18 • Soil Composition Monitoring • Soil Index • Salinity • Moisture • Real-time Observation Monitoring • Integration with External Sources e.g. weather, rainfall etc. IoT-Forum 2016, Reston, VA
  • 19. Scenario 2: Soil Fertility 14/12/2016 19 • Real-time Alert Notification System • Water Management • Right Time for cultivation • Fertilization IoT-Forum 2016, Reston, VA
  • 20. Conclusion 14/12/2016 20 • Agri-IoT offers real-time data analytics for smart farming applications. • Deployed a tested in medium-to-large farms (100-300 sensors deployed at the field). • Tested for performance and scalability for real-time stream processing and reasoning, based on IoT and semantic web technologies • Can help farmers in decision making and fast reactions to events happening. • A platform for innovation using Agri Data. IoT-Forum 2016, Reston, VA

Editor's Notes

  1. Smart Cities Citizens Perspective
  2. Smart Cities Administration View
  3. Smart Cities Citizens Perspective
  4. Smart Cities Citizens Perspective
  5. Smart Cities Citizens Perspective
  6. Smart Cities Citizens Perspective
  7. Smart Cities Citizens Perspective
  8. Smart Cities Citizens Perspective
  9. Smart Cities Citizens Perspective
  10. Smart Cities Citizens Perspective
  11. Smart Cities Citizens Perspective
  12. Smart Cities Citizens Perspective
  13. Smart Cities Citizens Perspective
  14. Smart Cities Citizens Perspective