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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

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