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Agri-IoT: A semantic framework for IoT-
enabled Smart Farming Applications
^ Unit for Reasoning, Querying and
Stream Proce...
Nowadays Sensors are almost Everywhere
14/12/2016
• Cheap and Light-weight
Sensors
• Easy Installation
• Sensors embedded ...
Sensors for Smart Farming
14/12/2016
• In smart farming, mostly
environmental sensors are
deployed
• Weather, Wind Speed, ...
Data Streams are Everywhere
14/12/2016
• Sensors produce
enormous amount of data
• Mostly in a streaming
fashion
• High ve...
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
• Mo...
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 taun...
Potential Achievements using Agri-IoT Platforms
• Precision agriculture:
• Improves productivity
• Reduce resource consump...
Agri-IoT: Real-time Data Analytics Platform
14/12/2016 11
• Integration of heterogeneous IoT data streams
• Provision of d...
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
d...
Agri-IoT: Software Components
14/12/2016 14
• Reusability of Components
– Data wrapper: (source: CityPulse)
– Device manag...
Agri-IoT: Semantic Annotation
14/12/2016 15
• SSN Ontology for Sensor Data Representation
• OWL-S for Service Description
...
Scenario 1: Fertility Management
14/12/2016 16
• Monitoring Breeding
Cycle
• Pre-mating Heat
Detection
• Real-time Monitor...
Scenario 1: Fertility Management
14/12/2016 17
• A single farm of 1,000
cows.
• Periodic heat detection
using wearable sen...
Scenario 2: Soil Fertility
14/12/2016 18
• Soil Composition
Monitoring
• Soil Index
• Salinity
• Moisture
• Real-time Obse...
Scenario 2: Soil Fertility
14/12/2016 19
• Real-time Alert
Notification System
• Water Management
• Right Time for cultiva...
Conclusion
14/12/2016 20
• Agri-IoT offers real-time data analytics for smart farming
applications.
• Deployed a tested in...
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Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications

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With the recent advancement of the Internet of Things (IoT), it is now possible to process a large number of sensor data streams using different large-scale IoT platforms. These IoT frameworks are used to collect, process and analyse data streams in real-time and facilitate provision of smart solutions
designed to provide decision support. Existing IoT-based solutions are mainly domain-dependent, providing stream processing and analytics focusing on specific areas (smart cities, healthcare etc.). In the context of agri-food industry, a variety of external parameters belonging to different domains (e.g. weather conditions, regulations etc.) have a major influence over the food supply chain, while flexible and adaptive IoT frameworks, essential to truly realize the concept of smart farming, are currently inexistent. In this presentation, we propose Agri-IoT, a semantic framework for IoT-based smart farming applications, which supports reasoning over
various heterogeneous sensor data streams in real-time. Agri-
IoT can integrate multiple cross-domain data streams, providing
a complete semantic processing pipeline, offering a common
framework for smart farming applications. Agri-IoT supports
large-scale data analytics and event detection, ensuring seamless interoperability among sensors, services, processes, operations, farmers and other relevant actors, including online information sources and linked open datasets and streams available on the Web.

Published in: Software

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

  1. 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. 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. 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. 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. 5. Smart Farming Applications 14/12/2016 5IoT-Forum 2016, Reston, VA
  6. 6. Smart Farming: An Overview 14/12/2016 6IoT-Forum 2016, Reston, VA
  7. 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. 8. IoT Platforms for Smart Farming: An Eco-System 14/12/2016 8IoT-Forum 2016, Reston, VA
  9. 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. 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. 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. 12. Agri-IoT: Real-time Data Analytics Platform 14/12/2016 12IoT-Forum 2016, Reston, VA
  13. 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. 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. 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. 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. 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. 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. 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. 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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